Range Weighted Branch Length Difference (RWiBaLD) Calculations & Figure Generation
Introduction
This document contains code for calculating and plotting Range Weighted Branch Length Difference (RWiBaLD). It uses input from Biodiverse analyses of phylogenetic data. The method is related to the Categorical Analysis of Neo And Paleo-Endemisim (CANAPE) published in 2014 and follows up on that paper. Consequently, the acacia dataset from the CANAPE paper is used to illustrate the method.
Load packages
Here we load the packages for use in subsequent code.
The RWiBaLD metric
The steps for calculating RWiBaLD are as follows:
For each branch on the phylogeny (terminal or internal), calculate the difference between its branch length on the Range Weighted Observed Tree (RWoT), and its branch length on the Range Weighted Comparison Tree (RWcT) to generate its Range Weighted Branch Length Difference (RWiBaLD) score.
Identify the branches that contribute the most to PE; we call these the “highly endemic branches” or “branches of key interest”. These are defined as those with the smallest observed range sizes, i.e. the longest branches on the RWcT. We calculate this by ranking all branches according to the inverse of their range size (functionally the same as their lengths on the RWcT), plotting a straight line from the first to last points on that curve, then identifying the point on the distribution that is furthest from that line (figure 2A). Weighted ranges greater than or equal to the threshold are considered highly endemic.
The same elbow statistic is then applied separately to each half of the distribution of ranked RWiBaLD score values (divided at the RWiBaLD = 0 value) to identify branch length difference thresholds.
The classification is then applied to the highly endemic branches of step 2 using the thresholds from step 3. Highly endemic branches with negative differences less than or equal to the threshold are classified as neo-endemic, positive differences greater than or equal to the threshold are classified as paleo-endemic, and those between these two categories are classified as meso-endemic (figure 2B).
Specify biodiverse results data
Specify the tabular data files exported from a biodiverse analysis. Both the observed data & the equal branch length data from a range weighted tree.
Load data & calculate RWiBaLD
Load the data in, merge the tables and calculate the RWiBaLD score for each branch, then write out the results to a file.
Show the code
# Load data
observed_data <- read.table(observed_data_csv, header=T,sep=",")
equal_branch_length_data <- read.table(equal_branch_length_data_csv, header=T,sep=",")
# Calculate RWiBaLD Statistic etc.
rwibald_results <- observed_data |>
left_join(equal_branch_length_data, by = 'NAME') |>
rename(branch_length_observed_tree = LENGTH.x, branch_length_comparison_tree = LENGTH.y) |>
mutate(rank_comparison_tree = rank(desc(branch_length_comparison_tree), ties.method = "min")) |>
mutate(sorted_asc_bl_comparison_tree = rank(branch_length_comparison_tree, ties.method = "first")) |>
mutate(rwibald_score = (branch_length_observed_tree - branch_length_comparison_tree)) |>
mutate(rwibald_score_rank = rank(rwibald_score, ties.method = "first"))
# View(rwibald_results)
output_dir <- "quarto_outputs/"
# Write data to file
write.csv(rwibald_results, paste0(output_dir, "Acacia_RWiBaLD_results.csv"), row.names=FALSE)Elbow point calculation function
We propose a function to calculate the elbow point of a curve in order to determine branches of interest and identify the different categories of RWiBaLD (e.g., neo, meso, paleo).
It takes a numerical vector data as input and calculates the point where the rate of decrease in a measure of fit (e.g., sum of squared distances) starts to slow down. The function first orders the values of the data and calculates vectors from the first point to all other points, normalizing the vector from the first to the last point. It then computes the Euclidean distance of each point to this line and returns the y-coordinate (value) of the point with the maximum distance from the line, considered the “elbow” point threshold.
The procedure is described step by step below:
Function Definition and Input:
- The function
get_elbowtakes one argument,data, which is a numerical vector.
- The function
Initial Setup:
n_ptsis assigned the length ofdata.x_coordsis a sequence from 1 ton_pts.y_coordsis the values ofdata, sorted in ascending order.
First Point Coordinates:
x1andy1are the coordinates of the first point in the sequence.
Normalize the Line Vector:
x_vecandy_vecare vectors from the first point to all other points.x_vec_maxandy_vec_maxare the vectors from the first point to the last point.normaliseris the length of the line vector.Normalize
x_vec_maxandy_vec_maxby dividing bynormaliser.
Vectors from First Point:
v_xandv_yare vectors from the first point to all other points.scalar_prodcalculates the scalar projection ofv_xandv_yonto the normalized line vector.
Calculate Distance to Line:
vec_to_line_xandvec_to_line_yare the components of the vectors from each point to the line.dist_to_lineis the Euclidean distance of each point to the line.
Return the Elbow Point Threshold:
- The function returns the y-coordinate (value) of the point with the maximum distance from the line, considered the “elbow” point threshold.
In summary, the function identifies the point in the data set that is farthest away from a line drawn between the first and last data points, which often corresponds to a significant change.
Show the code
get_elbow <- function(data){
n_pts = length(data)
x_coords = 1:n_pts
y_coords = data[order(data)]
# First points
x1 = x_coords[1]
y1 = y_coords[1]
# Normalize the line vector
x_vec = x_coords - x1
y_vec = y_coords - y1
x_vec_max = x_vec[n_pts]
y_vec_max = y_vec[n_pts]
normaliser = sqrt(x_vec_max^2 + y_vec_max^2)
x_vec_max = x_vec_max / normaliser
y_vec_max = y_vec_max / normaliser
# Vectors from first point
v_x = x_coords - x1
v_y = y_coords - y1
scalar_prod = v_x * x_vec_max + v_y * y_vec_max
vec_to_line_x = v_x - scalar_prod * x_vec_max
vec_to_line_y = v_y - scalar_prod * y_vec_max
# Distance to line is the norm
dist_to_line = sqrt(vec_to_line_x^2 + vec_to_line_y^2)
# y value at the point of maximum distance from the hypotenuse
return(y_coords[which(dist_to_line==max(dist_to_line))])
}Calculate branches of interest
Now we take the data and determine the key branches of interest using the elbow point statistic defined in the function above.
Show the code
# For range weighted tree
df <- rwibald_results[,c("NAME", "rwibald_score", "rwibald_score_rank",
"sorted_asc_bl_comparison_tree",
"branch_length_comparison_tree",
"branch_length_observed_tree")] %>%
arrange(., branch_length_comparison_tree)
# Call the get_elbow function to find the threshold
elbow_thresh <- get_elbow(df$branch_length_comparison_tree)
# Define key branches
key_branches <- df$branch_length_comparison_tree >= elbow_thresh
# Count them
number_of_key_branches <- sum(key_branches)
# Add a column to the results marking the branches of interest as 'sig'
rwibald_results_key <- rwibald_results %>%
arrange(., branch_length_comparison_tree) %>%
mutate(bl_comparison_tree_rwibald_elbow_key = ifelse(key_branches, 'key', 'not_key'))
# Merge data
rwibald_results_all <- merge(rwibald_results, rwibald_results_key[, c("NAME","bl_comparison_tree_rwibald_elbow_key")], by=c('NAME','NAME'),all.x=T)
# View(rwibald_results_all)
# Print number of key branches
print(paste0("Number of key branches: ", number_of_key_branches))[1] "Number of key branches: 135"
Generate figure 2A - RWiBaLD branches of interest
Now we plot the ranked branch lengths on the comparison tree showing the cut-off line for the key RWiBaLD branches of interest.
Show the code
# Check the total number of branches
# length(df$branch_length_comparison_tree)
# Add x_marks every 100
x_marks <- c(0, 100, 200, 300, 400, 500, 600, 700, 800, 900, length(df$branch_length_comparison_tree))
branch_length_by_rank <- ggplot(df, aes(x = as.numeric(rownames(df)), y = sort(branch_length_comparison_tree))) +
geom_point() +
geom_vline(xintercept = (length(df$branch_length_comparison_tree) - number_of_key_branches+1), color = "red", linetype = "dashed") +
xlab("Branch Length Rank (shortest-longest)") +
ylab("Branch Length\nComparison Tree (RWcT)") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5)) +
scale_x_continuous(breaks = x_marks, labels = x_marks) +
annotate("text", x = 970, y = 0.008, label = paste0(number_of_key_branches, " key\n branches\n of interest"), vjust = 1, hjust = 0.5)
print(branch_length_by_rank)Calculate RWiBaLD branch categories (neo-endemic, meso-endemic, paleo-endemic)
Next we take the key branches of interest and define which of them are neo-endemic, meso-endemic or paleo-endemic. We use the same elbow statistic defined above but only consider the branches of key interest calculated above as valid. We split the data at 0 with those equal or below the elbow point threshold in the negative data being neo-endemics. Those in the positive dataset equal or above the elbow point threshold being classified as paleo-endemic, and rest are classified as meso-endemic. We have described three categories, but of course one could break the continuous distribution into further categories if required.
Show the code
# Load the data
data <- rwibald_results_all[,c("NAME", "bl_comparison_tree_rwibald_elbow_key", "rwibald_score", "rwibald_score_rank")] %>%
arrange(., rwibald_score_rank)
# Subset the left and right dataframes
subset_left_df <- data[data$rwibald_score <= 0, ]
subset_right_df <- data[data$rwibald_score >= 0, ]
# Create a list to store the results for the table
results <- list()
# Records in negative data
negative_records <- nrow(subset_left_df)
results$Negative_Records <- negative_records
# Neo cutoff
left_point_x <- get_elbow(subset_left_df$rwibald_score)
rwibald_score_rank_at_left_point <- subset_left_df[subset_left_df$rwibald_score == left_point_x, "rwibald_score_rank"]
results$Neo_Cutoff <- rwibald_score_rank_at_left_point
# Plot for left half of curve
l <- ggplot(subset_left_df, aes(x = rwibald_score_rank, y = rwibald_score)) +
geom_point() +
geom_vline(xintercept = rwibald_score_rank_at_left_point, color = "red", linetype = "dashed") +
ggtitle(label= "Neo cutoff") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
print(l)Show the code
# Records in positive data
positive_records <- nrow(subset_right_df)
results$Positive_Records <- positive_records
# Paleo cutoff
right_point_x <- get_elbow(subset_right_df$rwibald_score)
rwibald_score_rank_at_right_point <- subset_right_df[subset_right_df$rwibald_score == right_point_x, "rwibald_score_rank"]
results$Paleo_Cutoff <- rwibald_score_rank_at_right_point
# Plot for right half of curve
r <- ggplot(subset_right_df, aes(x = rwibald_score_rank, y = rwibald_score)) +
geom_point() +
geom_vline(xintercept = rwibald_score_rank_at_right_point, color = "red", linetype = "dashed") +
ggtitle(label= "Paleo cutoff") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
print(r)Show the code
# Create a column with the results
rwibald_results_key_type <- rwibald_results_all %>%
filter(bl_comparison_tree_rwibald_elbow_key == "key") %>%
mutate(rwibald_type = case_when(rwibald_score_rank <= rwibald_score_rank_at_left_point ~ "neo-endemic",
rwibald_score_rank >= rwibald_score_rank_at_right_point ~ "paleo-endemic",
TRUE ~ "meso-endemic")) %>%
select(NAME, rwibald_type)
# Convert the results list to a dataframe for gt
summary_results_df <- as.data.frame(t(sapply(results, c)))
results_gt <- gt(data = summary_results_df)
# Display the table
results_gt %>%
tab_header(title = "Summary of Records and Cutoffs") %>%
fmt_number(columns = everything(), decimals = 0) %>%
tab_style(style = cell_text(align = "center"),
locations = cells_body()
)
# View(rwibald_results_key_type)
# Merge the data
rwibald_results_all <- merge(rwibald_results_all, rwibald_results_key_type[, c("NAME","rwibald_type")], by=c('NAME','NAME'),all.x=T)
# View(rwibald_results_all)
output_dir <- "quarto_outputs/"
# Write data to file
write.csv(rwibald_results_all,paste0(output_dir, "Acacia_RWiBaLD_results_all.csv") ,row.names=FALSE)| Summary of Records and Cutoffs | |||
|---|---|---|---|
| Negative_Records | Neo_Cutoff | Positive_Records | Paleo_Cutoff |
| 686 | 71 | 330 | 991 |
Load RWiBaLD results & tree data by cells
Now we load the data for the RWiBaLD results and the range weighted tree data in tabular format and then replace the counts in the matrix with the branch lengths for that group so we can generate the histograms for each grid cell location. We also add the CANAPE scores to the data table for comparison and calculate the ranges of each branch.
Show the code
data_dir <- "Acacia_biodiverse_exports/"
output_dir <- "quarto_outputs/"
# Data with internal branches (spatial anaylsis from BD) x grid cells
all_tree_data_csv <- paste0(data_dir, "Acacia_PD_Included_Node_List.csv")
RWiBaLD_results_csv <- paste0(output_dir, "Acacia_RWiBaLD_results_all.csv")
all_tree_data <- read.table(all_tree_data_csv, header=T, sep=",", check.names = FALSE )
RWiBaLD_results <- read.table(RWiBaLD_results_csv, header=T, sep=",")
#View(all_tree_data)
#View(RWiBaLD_results)
valueToUse <- "rwibald_score"
# Iterate over the columns in Table 1
for (col in colnames(all_tree_data)[-c(1:3)]) {
# get the corresponding "valueToUse" column specified above from Table 2
branch_length <- RWiBaLD_results[RWiBaLD_results$NAME == col, paste0(valueToUse)]
# replace the numbers in Table 1 with the "valueToUse" value
all_tree_data[, col] <- ifelse(is.na(all_tree_data[, col]), NA, as.numeric(branch_length))
}
# View(all_tree_data)
# Write data to file
write.csv(all_tree_data, paste0(output_dir, "Acacia_PD_Included_Node_List_", valueToUse, ".csv"), row.names=FALSE)
# Get CANAPE data from biodiverse as well
canape_csv <- paste0(data_dir, "Acacia_Rand1_CANAPE_Export.csv")
canape_results <- read.table(canape_csv, header=T,sep=",")
# View(canape_results)
canape_group_data <- canape_results %>%
left_join(all_tree_data %>%
select(-c(Axis_0, Axis_1)), by = "ELEMENT"
)
# Transpose the dataframe
data <- t(canape_group_data)
# View(data)
# Set the first row as column names
transposed_df <- setNames(data.frame(data[-1,]), data[1,])
# View(transposed_df)
# Merge CANAPE and RWiBaLD data
canape_group_data_rwibald <- transposed_df %>%
mutate(range_cell_count = rowSums(!is.na(.))) %>%
tibble::rownames_to_column(var = "NAME") %>%
left_join(select(rwibald_results_all, NAME, rwibald_type), by = 'NAME') %>%
mutate(rwibald_type = ifelse(is.na(rwibald_type), "other", rwibald_type))
# Set row names
rownames(canape_group_data_rwibald) <- canape_group_data_rwibald$NAME
# View(canape_group_data_rwibald)
# # Add range data to results
# rwibald_results_all_with_range <- rwibald_results_all %>%
# left_join(select(canape_group_data_rwibald, NAME, range_cell_count),by = "NAME") %>%
# mutate(rwibald_type = replace(rwibald_type,is.na(rwibald_type),"other"))
# Add range data to results and create three new columns
rwibald_results_all_with_range <- rwibald_results_all %>%
left_join(select(canape_group_data_rwibald, NAME, range_cell_count), by = "NAME") %>%
mutate(rwibald_type = replace(rwibald_type, is.na(rwibald_type), "other"),
non_range_weighted_branch_length_observed_tree = branch_length_observed_tree * range_cell_count,
non_range_weighted_branch_length_comparison_tree = branch_length_comparison_tree * range_cell_count,
diff_non_range_weighted_observed_to_comparison_bl = non_range_weighted_branch_length_observed_tree - non_range_weighted_branch_length_comparison_tree
) %>%
mutate(rank_BaLD = rank(diff_non_range_weighted_observed_to_comparison_bl, ties.method = "first"))
# Write data to file
write.csv(rwibald_results_all_with_range, paste0(output_dir, "Acacia_RWiBaLD_results_all_with_range.csv"), row.names=FALSE)
# View(rwibald_results_all_with_range)
# View(rwibald_results_all)Generate figure 2B - ranked RWiBaLD score by RWiBaLD score
Here we plot the ranked RWiBaLD score (x) by the RWiBaLD score (y) (Figure 2B), colouring the different RWiBaLD Categories: neo-endemic (red), meso-endemic (darkolivegreen4) and paleo-endemic (royalblue1).
Show the code
# Find the neo_cutoff
neo_cutoff <- rwibald_results_all_with_range %>%
filter(rwibald_type == "neo-endemic") %>%
summarize(highest_rank = max(rwibald_score_rank, na.rm = TRUE)) %>%
pull(highest_rank)
# Find the paleo_cutoff
paleo_cutoff <- rwibald_results_all_with_range %>%
filter(rwibald_type == "paleo-endemic") %>%
summarize(lowest_rank = min(rwibald_score_rank, na.rm = TRUE)) %>%
pull(lowest_rank)
# Get only the rwibald branches of key interest
rwibald_results_all_key_only <- rwibald_results_all_with_range %>%
filter(bl_comparison_tree_rwibald_elbow_key == "key")
# View(rwibald_results_all_key_only)
# View(rwibald_results_all_with_range)
# Plot rwibald rank X rwibald score
rwibald_key_plot <- ggplot() +
geom_point(data=rwibald_results_all_with_range, aes(x = rwibald_score_rank, y = rwibald_score), fill="transparent", size = 3, colour= "grey77", alpha = 0.4, pch = 21) +
geom_point(data = rwibald_results_all_key_only, aes(x = rwibald_score_rank, y = rwibald_score, fill = rwibald_type, color = rwibald_type), size = 3, alpha = 0.9, pch = 21) +
scale_fill_manual(name = "RWiBaLD Category", values = c("paleo-endemic" = "royalblue1", "neo-endemic" = "red", "meso-endemic" = "darkolivegreen4"), labels = c("neo-endemic","meso-endemic", "paleo-endemic"), limits = c("neo-endemic","meso-endemic", "paleo-endemic"), na.value = "transparent") +
scale_color_manual(name = "RWiBaLD Category", values = c("paleo-endemic" = "royalblue4", "neo-endemic" = "red4", "meso-endemic" = "darkolivegreen"), labels = c("neo-endemic", "meso-endemic", "paleo-endemic"), limits = c("neo-endemic", "meso-endemic", "paleo-endemic"), na.value = "transparent") +
geom_vline(xintercept = neo_cutoff, color = "red", linetype = "dashed") +
geom_vline(xintercept = paleo_cutoff, color = "red", linetype = "dashed") +
xlab("RWiBaLD Score Rank (shortest-longest)") +
ylab("Range Weighted Branch\nLength Difference (RWiBaLD) Score") +
#annotate("segment", x = results$Negative_Records, xend = results$Negative_Records,
# y = max(rwibald_results_all_with_range$rwibald_score) * 1.1, yend = 0,
# arrow = arrow(length = unit(0.2, "cm")), color = "grey74") +
geom_vline(xintercept = results$Negative_Records, color = "grey74", linetype = "solid") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5), legend.position="bottom", legend.text = element_text(size = 12))
print(rwibald_key_plot)Generate figure 2C - ranked BaLD by BaLD (non range weighted)
Illustrating the distribution of branch lengths on the observed tree with no range-weighting; the Y-axis is the difference between length on the observed tree and length on the comparison tree for each branch; the X-axis is ranked branch length difference (shortest - longest). The branches of interest are colored by RWiBaLD category as in 2B
Show the code
#View(rwibald_results_all_with_range)
#View(rwibald_results_all_key_only)
# Plot non_range_weighted score
rwibald_key_plot_non_range_weighted <- ggplot() +
#geom_point(data=rwibald_results_all_with_range, aes(x = reorder(non_range_weighted_branch_length_observed_tree, non_range_weighted_branch_length_observed_tree), y = diff_non_range_weighted_observed_to_comparison_bl), fill="transparent", size = 3, colour= "grey77", alpha = 0.4, pch = 21) +
geom_point(data=rwibald_results_all_with_range, aes(x = rank_BaLD, y = diff_non_range_weighted_observed_to_comparison_bl), fill="transparent", size = 3, colour= "grey77", alpha = 0.4, pch = 21) +
geom_point(data = rwibald_results_all_key_only, aes(x = rank_BaLD, y = diff_non_range_weighted_observed_to_comparison_bl, fill = rwibald_type, color = rwibald_type), size = 3, alpha = 0.9, pch = 21) +
scale_fill_manual(name = "RWiBaLD Category", values = c("paleo-endemic" = "royalblue1", "neo-endemic" = "red", "meso-endemic" = "darkolivegreen4"), labels = c("neo-endemic","meso-endemic", "paleo-endemic"), limits = c("neo-endemic","meso-endemic", "paleo-endemic"), na.value = "transparent") +
scale_color_manual(name = "RWiBaLD Category", values = c("paleo-endemic" = "royalblue4", "neo-endemic" = "red4", "meso-endemic" = "darkolivegreen"), labels = c("neo-endemic", "meso-endemic", "paleo-endemic"), limits = c("neo-endemic", "meso-endemic", "paleo-endemic"), na.value = "transparent") +
#geom_vline(xintercept = neo_cutoff, color = "red", linetype = "dashed") +
#geom_vline(xintercept = paleo_cutoff, color = "red", linetype = "dashed") +
xlab("Unweighted Branch Length Difference (BaLD) Score Rank (shortest−longest)") +
ylab("Branch Length\nDifference (BaLD) Score") +
geom_vline(xintercept = results$Negative_Records, color = "grey74", linetype = "solid") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5), legend.position="none", legend.text = element_text(size = 12), axis.title.x = element_text(size = 12), axis.text.x = element_text(size = 8))
print(rwibald_key_plot_non_range_weighted)Generate multi-panel figure 2ABC
Here we generate a 2 up figure using patchwork
Show the code
patchwork <- branch_length_by_rank / rwibald_key_plot / rwibald_key_plot_non_range_weighted + plot_annotation(
#title = 'Branches of Interest for Range Weighted Branch Length Difference (RWiBaLD)\n&\nRWiBaLD Scores With Categories Calculated Using Elbow Statistic',
theme = theme(plot.title = element_text(hjust = 0.5)),
subtitle = '',
caption = '',
tag_levels = 'A') +
theme(plot.tag.position = c(0, 1),
plot.tag = element_text(size = 12, hjust = 0, vjust = 0))
#print(patchwork)
ggsave("quarto_outputs/figures/Figure2_ABC.png", patchwork, width = 3000, height = 3600, units = "px")Generate Figure 3A
Here we create a plot of RWiBaLD score (x) by branch range (cells) (Figure 3A), coloured by RWiBaLD Categories: neo-endemic (red), meso-endemic (green) and paleo-endemic (blue).
Show the code
# Plot RWiBaLD rank X branch range
rwibald_key_plot_range <- ggplot() +
geom_point(data=rwibald_results_all_with_range, aes(x = rwibald_score_rank, y = range_cell_count), fill="transparent", size = 2, colour= "grey77", alpha = 0.4, pch = 21) +
geom_point(data = rwibald_results_all_key_only, aes(x = rwibald_score_rank, y = range_cell_count, fill = rwibald_type, color = rwibald_type), size = 2, alpha = 0.9, pch = 21) +
scale_fill_manual(name = "RWiBaLD Category", values = c("paleo-endemic" = "royalblue1","neo-endemic" = "red", "meso-endemic" = "darkolivegreen4"), labels = c("neo-endemic","meso-endemic", "paleo-endemic"), limits = c("neo-endemic","meso-endemic", "paleo-endemic"), na.value = "transparent") +
scale_color_manual(name = "RWiBaLD Category", values = c("paleo-endemic" = "royalblue4", "neo-endemic" = "red4", "meso-endemic" = "darkolivegreen"), labels = c("neo-endemic", "meso-endemic", "paleo-endemic"), limits = c("neo-endemic", "meso-endemic", "paleo-endemic"), na.value = "transparent") +
geom_vline(xintercept = neo_cutoff, color = "red", linetype = "dashed") +
geom_vline(xintercept = paleo_cutoff, color = "red", linetype = "dashed") +
geom_vline(xintercept = results$Negative_Records, color = "grey74", linetype = "solid") +
xlab("RWiBaLD Score Rank (shortest-longest)") +
ylab("Branch Range (cells)") +
theme_classic() +
theme(plot.title = element_text(hjust =1),
legend.position="none",
legend.text = element_text(size = rel(1)),
legend.title = element_text(size = rel(1)),
axis.title.x = element_text(size = rel(1)),
axis.title.y = element_text(size = rel(1)),
axis.text.x = element_text(size = rel(1)),
axis.text.y = element_text(size = rel(1)))
print(rwibald_key_plot_range)Generate Figure 3B
Now the same data as figure 3A but first removing all internal branches before plotting the data. Note: the neo / meso /paleo cuttofs are calculated on the whole dataset and the filtering to terminals done after that.
Show the code
# For plotting purposes get only the rwibald branches of key interest and terminals only
rwibald_results_all_key_only_terminals <- rwibald_results_all_with_range %>%
filter(!grepl("^[0-9]", NAME)) %>%
mutate(rwibald_score_rank_terminals = rank(rwibald_score, ties.method = "first")) %>%
filter(bl_comparison_tree_rwibald_elbow_key == "key")
rwibald_results_all_with_range_terminals <- rwibald_results_all_with_range %>%
filter(!grepl("^[0-9]", NAME)) %>%
mutate(rwibald_score_rank_terminals = rank(rwibald_score, ties.method = "first"))
# Find the neo_cutoff
neo_cutoff_terms <- rwibald_results_all_with_range_terminals %>%
filter(rwibald_type == "neo-endemic") %>%
summarize(highest_rank = max(rwibald_score_rank_terminals, na.rm = TRUE)) %>%
pull(highest_rank)
# Find the paleo_cutoff
paleo_cutoff_terms <- rwibald_results_all_with_range_terminals %>%
filter(rwibald_type == "paleo-endemic") %>%
summarize(lowest_rank = min(rwibald_score_rank_terminals, na.rm = TRUE)) %>%
pull(lowest_rank)
# Subset the left and right dataframes
subset_left_df_terms <- rwibald_results_all_with_range_terminals[rwibald_results_all_with_range_terminals$rwibald_score <= 0, ]
# Records in negative data
negative_records_terms <- nrow(subset_left_df_terms)
# View(rwibald_results_all_key_only_terminals)
# View(rwibald_results_all_with_range_terminals)
# View(rwibald_results_all_with_range)
# Plot rwibald rank x rwibald score
# rwibald_key_plot_terminals <- ggplot() +
# geom_point(data=rwibald_results_all_with_range_terminals, aes(x = rwibald_score_rank_terminals, y = rwibald_score), fill="transparent", size =3, colour= "grey77", alpha = 0.4, pch=21) +
# geom_point(data = rwibald_results_all_key_only_terminals, aes(x = rwibald_score_rank_terminals, y = rwibald_score, fill = rwibald_type, color = rwibald_type), size =3, alpha = 0.9, pch=21) +
# scale_fill_manual(name = "RWiBaLD Category", values = c("paleo-endemic" = "royalblue1", "neo-endemic" = "red", "meso-endemic" = "darkolivegreen4"), labels = c("neo-endemic","meso-endemic", "paleo-endemic"), limits = c("neo-endemic","meso-endemic", "paleo-endemic"), na.value = "transparent") +
# scale_color_manual(name = "RWiBaLD Category", values = c("paleo-endemic" = "royalblue4", "neo-endemic" = "red4", "meso-endemic" = "darkolivegreen"), labels = c("neo-endemic", "meso-endemic", "paleo-endemic"), limits = c("neo-endemic", "meso-endemic", "paleo-endemic"), na.value = "transparent") +
# geom_vline(xintercept = neo_cutoff, color = "red", linetype = "dashed") +
# geom_vline(xintercept = paleo_cutoff, color = "red", linetype = "dashed") +
# xlab("RWiBaLD Score Rank Terminals Only (shortest-longest)") +
# ylab("Range Weighted Branch\nLength Difference (RWiBaLD) Score") +
# theme_classic() +
# theme(plot.title = element_text(hjust = 0.5), legend.position="bottom", legend.text = element_text(size = 12))
#
# print(rwibald_key_plot_terminals)
#
# ggsave("quarto_outputs/figures/Figure3_B_terminals_only.png", plot = rwibald_key_plot_terminals, scale = 1,
# width = 2048,
# height = 1024,
# units = "px",
# dpi = 300)
# Plot rwibald rank x branch range
rwibald_key_plot_range_terminals <- ggplot() +
geom_point(data=rwibald_results_all_with_range_terminals, aes(x = rwibald_score_rank_terminals, y = range_cell_count), fill="transparent", size =3, colour= "grey77", alpha = 0.4, pch=21) +
geom_point(data = rwibald_results_all_key_only_terminals, aes(x = rwibald_score_rank_terminals, y = range_cell_count, fill = rwibald_type, color = rwibald_type), size =3, alpha = 0.9, pch=21) +
scale_fill_manual(name = "RWiBaLD Category", values = c("paleo-endemic" = "royalblue1","neo-endemic" = "red", "meso-endemic" = "darkolivegreen4"), labels = c("neo-endemic","meso-endemic", "paleo-endemic"), limits = c("neo-endemic","meso-endemic", "paleo-endemic"), na.value = "transparent") +
scale_color_manual(name = "RWiBaLD Category", values = c("paleo-endemic" = "royalblue4", "neo-endemic" = "red4", "meso-endemic" = "darkolivegreen"), labels = c("neo-endemic", "meso-endemic", "paleo-endemic"), limits = c("neo-endemic", "meso-endemic", "paleo-endemic"), na.value = "transparent") +
geom_vline(xintercept = neo_cutoff_terms, color = "red", linetype = "dashed") +
geom_vline(xintercept = paleo_cutoff_terms, color = "red", linetype = "dashed") +
geom_vline(xintercept = negative_records_terms, color = "grey74", linetype = "solid") +
xlab("RWiBaLD Score Rank Terminals Only (shortest-longest)") +
ylab("Branch Range (cells)") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5), legend.position="bottom", legend.text = element_text(size=rel(1)))
print(rwibald_key_plot_range_terminals)Generate multi-panel figure 3AB
Show the code
patchwork <- rwibald_key_plot_range / rwibald_key_plot_range_terminals + plot_annotation(
theme = theme(plot.title = element_text(hjust = 0.5)),
subtitle = '',
caption = '',
tag_levels = 'A') +
theme(plot.tag.position = c(0, 1),
plot.tag = element_text(size = 12, hjust = 0, vjust = 0))
#print(patchwork)
ggsave("quarto_outputs/figures/Figure3_AB.png", patchwork, width = 3000, height = 3600, units = "px")Read tree file & generate supplementary figure 1
Now we load the range weighted tree file exported from biodiverse and colour code it by the RWiBaLD categories.
Show the code
# if need be check and install ggtree using this code
# if (!require("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#
# BiocManager::install("ggtree")
data_dir <- "Acacia_biodiverse_exports/"
all_tree_data_nwk <- paste0(data_dir, "acacia_tree_TRIMMED1_EQ1_RW1.nwk")
myTree <- read.tree(file=all_tree_data_nwk)
rwibald_results_all <- rwibald_results_all %>%
mutate(rwibald_type = replace(rwibald_type,is.na(rwibald_type),"other"))
# View(rwibald_results_all)
rwibald_results_all$tree_cols <- as.factor(rwibald_results_all$rwibald_type)
# Remove quotes from labels so they match dataframe
myTree$tip.label <- gsub("'","",myTree$tip.label)
myTree$node.label <- gsub("'","",myTree$node.label)
# Create ggtree object
g <- ggtree(myTree) %<+% rwibald_results_all +
aes(color=tree_cols) +
scale_color_manual(values = c("paleo-endemic" = "royalblue1",
"neo-endemic" = "red",
"meso-endemic" = "darkolivegreen")) +
# geom_text2(aes(label = node),
# hjust =0, # adjust horizontal position
# vjust = 0, # adjust vertical position
# size = 1, # adjust text size
# color = "blue") + # adjust text color
theme(legend.position="none") +
geom_tiplab(as_ylab=FALSE, size=1)
# Display the plot
#print(g)
ggsave("quarto_outputs/figures/SupFigure1_full_tree.png", g, width = 2048,
height = 4024, units = "px")Generate tree in 2 halves supplementary figure 1A and 1B
Show the code
data_dir <- "Acacia_biodiverse_exports/"
all_tree_data_nwk <- paste0(data_dir, "acacia_tree_TRIMMED1_EQ1_RW1.nwk")
myTree <- read.tree(file=all_tree_data_nwk)
rwibald_results_all <- rwibald_results_all %>%
mutate(rwibald_type = replace(rwibald_type, is.na(rwibald_type), "other"))
rwibald_results_all$tree_cols <- as.factor(rwibald_results_all$rwibald_type)
# Remove quotes from labels so they match the dataframe
myTree$tip.label <- gsub("'", "", myTree$tip.label)
myTree$node.label <- gsub("'", "", myTree$node.label)
# Calculate the halfway point
half <- length(myTree$tip.label) / 2
# Sort the tip labels for consistency
tip_labels <- myTree$tip.label
first_half_tips <- tip_labels[1:half]
second_half_tips <- tip_labels[(half + 1):length(tip_labels)]
# Identify MRCA nodes for each half
node_first_half <- getMRCA(myTree, first_half_tips)
node_second_half <- getMRCA(myTree, second_half_tips)
# Create the base ggtree object
g <- ggtree(myTree) %<+% rwibald_results_all +
aes(color = tree_cols) +
scale_color_manual(values = c("paleo-endemic" = "royalblue1",
"neo-endemic" = "red",
"meso-endemic" = "darkolivegreen",
"other" = "gray66")) +
theme(legend.position = "none") +
geom_tiplab(as_ylab = FALSE, size=1)
#plot(g)
# Identify MRCA node
mrca_node <- MRCA(g, second_half_tips)
second_half_tree <- viewClade(tree_view = g, mrca_node) +
geom_point2(
aes(subset = (node == mrca_node)),
shape = 21,
size = 2,
fill = 'gray26',
nudge_x = 20,
position = position_nudge(y = -88))
# +
# theme(panel.ontop = FALSE,
# panel.spacing.x = unit(c(1, 1, 1, 1), "cm"),
# panel.background = element_rect(color = "green", fill="transparent", linewidth = 10),
# plot.margin = unit(c(1, 1, 1, 1), "cm"))
#plot(second_half_tree)
# Save the figure showing the second half expanded and the first half collapsed
ggsave("quarto_outputs/figures/SupFigure_1A.png",
second_half_tree, width = 2048, height = 4024, units = "px")
#plot(g)
# Collapse the second half of the tree
g_half_collapsed <- collapse(g, node = node_second_half) +
# Add a point to indicate the collapsed node for reference
geom_point2(aes(subset=(node == node_second_half)), shape=21, size=2, fill='gray26')
#plot(g_half_collapsed)
# Save the figure showing the first half expanded and the second half collapsed
ggsave("quarto_outputs/figures/SupFigure_1B.png",
g_half_collapsed, width = 2048, height = 4024, units = "px")Load data & generate histograms for all cells
Next we load the data and generate histograms. This code generates a histogram for every cell in the dataset. For convenience the file names contain the CANAPE code and whether the histogram contains branches of key interest.
Show the code
#function to change decimal places displayed on plot
scaleFUN <- function(x) sprintf("%.8g", x)
# Function to replace '-' with 'm' and ':' with '_'
clean_column_names <- function(df) {
new_names <- colnames(df) %>%
gsub("-", "neg", .) %>%
gsub(":", "_", .)
colnames(df) <- new_names
return(df)
}
# Clean column names using the function
canape_group_data_rwibald <- clean_column_names(canape_group_data_rwibald)
#View(canape_group_data_rwibald)
col_scheme <- c("paleo-endemic" = "royalblue1","neo-endemic" = "red", "meso-endemic" = "darkolivegreen", "other" = "lightgoldenrodyellow")
#legend_labels <- c("neo-endemic"="Neo Endemic","paleo-endemic"="paleo Endemic", "other" = "other", "meso-endemic" = "Meso Endemic")
#data <- transposed_df
#View(canape_key_group_data_rwibald)
#Generate all histograms
for (i in 2:(ncol(canape_group_data_rwibald)-2)) {# skip first column and last 2 as they are NAME,cell_count,rwibald_type
#for (i in 74:74) {
#i <- 74
data <- canape_group_data_rwibald[-(1:6),] # remove the rows not needed
col_name <- colnames(data)[i] # get the column name to generate the histogram of
colourByCol <- "rwibald_type" # set the column to use for colouring the histogram
numberOfBins <- 41 # set number of bins to use
hist_data <- na.omit(as.numeric(data[[col_name]])) # extract that column data for ploting
hist_range <- range(hist_data,na.rm=TRUE) # calculate the range of all values in the column ie. min/max value
#calculate the total range of the whole data set to get a consistent x scale on all histograms.
dataForRange <- data %>%
select(-first(colnames(.)), -last(colnames(.))) %>%
mutate(across(everything(), as.numeric))
totalRange <- range(dataForRange, na.rm=TRUE)#lowest and highedt values in the dataset
totalRangeGap <- max(dataForRange, na.rm = TRUE) - min(dataForRange, na.rm = TRUE) # distance between range above
range <- max(hist_data) - min(hist_data)
binSize <- range/numberOfBins
farthest_number <- max(abs(hist_range)) # calculate the number farthest from 0
x_min <- -(farthest_number)-2*(binSize) # set xmin so zero is centred
x_max <- (farthest_number)+2*(binSize) # set xmax so zero is centred
#check to see if any of the branches in the histogram are RWIBALD significant to add to the file names
result <- data %>%
select(!!sym(col_name), !!sym(colourByCol)) %>%
filter(!is.na(!!sym(col_name))) %>%
summarise(result = ifelse(any(!!sym(colourByCol) %in% c("paleo-endemic", "neo-endemic", "meso-endemic")), "key", "zero-key")) %>%
pull(result)
#View(result)
# setup the file names
CANAPE_CODE <- canape_group_data_rwibald["CANAPE_CODE", col_name]
cell <- str_replace_all(col_name, "[:]", "_")
filename <- paste0("quarto_outputs/histograms/", cell, "_CC_", CANAPE_CODE,"_", result, "_", numberOfBins,"bins.png")
#print(filename)
# get a subset of dataframe for the histogram
plot_data <- data %>%
select(!!sym(col_name), !!sym(colourByCol)) %>%
filter(!is.na(!!sym(col_name))) %>%
arrange(!!sym(colourByCol))
#View(plot_data)
plot <- ggplot(plot_data, aes(x = as.numeric(.data[[col_name]]), fill = forcats::fct_rev(rwibald_type))) +
geom_histogram(color = "black", linewidth= 0.2, bins = numberOfBins) +
scale_fill_manual(values = col_scheme) +
xlim(x_min, x_max) +
coord_cartesian(ylim = c(0, 20)) +
labs(x = "RWiBaLD Score", y = "Frequency", fill = "Branch Category") +
theme_bw()
plot <- plot +
stat_bin(
aes(label = after_stat(if_else (condition = count>20, as.character(count), ""))),
bins= as.numeric(numberOfBins),
position=position_stack(vjust=0.1),
pad=TRUE,
geom = "text",
color = "black",
size = 2,
y = 19
)
CairoPNG(width = 1024, height = 600, file = filename, canvas="white", bg = "white", units="px", dpi=96, title = "")
print(plot)
dev.off()
}Load data & generate 12 histograms for figure 4A-L
This code filters out only the 12 histograms included in Figure 4 and generates the ggplot2 objects of them.
Show the code
data <- t(canape_group_data)
#View(data)
#Transpose the dataframe and set the first row as column names
transposed_df <- setNames(data.frame(data[-1,]), data[1,])
#View(transposed_df)
#cells to keep
cols_to_keep <- c("NAME", "-1825000:-2975000", "75000:-2525000", "625000:-3475000","-1425000:-3225000","1775000:-3725000", "-1425000:-3475000", "-1225000:-3775000", "-1425000:-3275000", "1225000:-1275000", "-1175000:-3275000", "-1075000:-2825000", "-1075000:-2375000")
#Calculate RWiBaLD significants and Statistics etc.
canape_group_data_rwibald <- transposed_df %>%
tibble::rownames_to_column(var = "NAME") %>%
select(all_of(cols_to_keep)) %>%
left_join(select(rwibald_results_all, NAME, rwibald_type), by = 'NAME') %>%
mutate(rwibald_type = ifelse(is.na(rwibald_type), "other", rwibald_type))
# Check if the column is already a factor
if (!is.factor(canape_group_data_rwibald$rwibald_type)) {
# If not a factor, convert and specify levels
canape_group_data_rwibald$rwibald_type <- factor(canape_group_data_rwibald$rwibald_type, levels = c("neo-endemic", "meso-endemic", "paleo-endemic", "other"))
}
#View(canape_group_data_rwibald)
# Function to replace '-' with 'm' and ':' with '_'
clean_column_names <- function(df) {
new_names <- colnames(df) %>%
gsub("-", "neg", .) %>%
gsub(":", "_", .)
colnames(df) <- new_names
return(df)
}
# Clean column names using the function
canape_group_data_rwibald <- clean_column_names(canape_group_data_rwibald)
rownames(canape_group_data_rwibald) <- canape_group_data_rwibald$NAME
#View(canape_group_data_rwibald)
# Write data to file
write.csv(canape_group_data_rwibald, paste0("quarto_outputs/transposed_df_figure_only.csv"), row.names=FALSE)
#function to change decimal places displayed on plot
scaleFUN <- function(x) sprintf("%.8g", x)
#View(canape_group_data_rwibald)
col_scheme <- c("paleo-endemic" = "royalblue1",
"neo-endemic" = "red",
"meso-endemic" = "darkolivegreen",
"other" = "lightgoldenrodyellow"
)
legend_labels <- c("neo-endemic" = "neo-endemic",
"paleo-endemic" = "paleo-endemic",
"meso-endemic" = "meso-endemic",
"other" = "other"
)
legend_order <- c("neo-endemic", "meso-endemic", "paleo-endemic", "other")
#data <- transposed_df
#View(canape_group_data_rwibald)
# Create an empty list to store plots
plot_list <- list()
#Generate histograms
for (i in 2:(ncol(canape_group_data_rwibald)-1)) {# skip first column and last 2 as they are NAME,rwibald_type
#i <- 2
local({ # have to make this local to allow multiple plots in patchwork
i <- i
data <- canape_group_data_rwibald[-(1:6),] # remove the rows not needed
col_name <- colnames(data)[i] # get the column name to generate the histogram of
colourByCol <- "rwibald_type" # set the column to use for colouring the histogram
numberOfBins <- 41 # set number of bins to use
# have to show only one legend as the 'collect' feature in patchwork does not collect the legends as desired
if(i == 3){
showLegend <- TRUE
} else {
showLegend <- FALSE
}
hist_data <- na.omit(as.numeric(data[[col_name]])) # extract that column data for ploting
hist_range <- range(hist_data,na.rm=TRUE) # calculate the range of all values in the column ie. min/max value
#calculate the total range of the whole data set to get a consistent x scale on all histograms.
dataForRange <- data %>%
select(-first(colnames(.)), -last(colnames(.))) %>%
mutate(across(everything(), as.numeric))
totalRange <- range(dataForRange, na.rm=TRUE)#lowest and highest values in the dataset
totalRangeGap <- max(dataForRange, na.rm = TRUE) - min(dataForRange, na.rm = TRUE) # distance between range above
range <- max(hist_data) - min(hist_data)
binSize <- range/numberOfBins
farthest_number <- max(abs(hist_range)) # calculate the number farthest from 0
x_min <- -(farthest_number)-2*(binSize) # set xmin so zero is centred
x_max <- (farthest_number)+2*(binSize) # set xmax so zero is centred
# get a subset of dataframe for the histogram
plot_data <- data %>%
mutate(!!sym(col_name) := as.numeric(!!sym(col_name))) %>%
select(!!sym(col_name), !!sym(colourByCol)) %>%
filter(!is.na(!!sym(col_name))) %>%
arrange(!!sym(colourByCol))
#View(plot_data)
#str(plot_data)
plot <- ggplot(plot_data, aes(x = .data[[col_name]], fill = forcats::fct_rev(rwibald_type))) +
geom_histogram(color = "black", linewidth = 0.2, bins = numberOfBins, show.legend = showLegend) +
scale_fill_manual(values = col_scheme, labels = legend_labels, breaks = legend_order, drop = FALSE, na.value = "transparent") +
#xlim(x_min, x_max) +
xlim(-0.011, 0.011) +
coord_cartesian(ylim = c(0, 20)) +
labs(x = "RWiBaLD Score", y = "Frequency", fill = "RWiBaLD Category") +
theme_bw()
plot <- plot +
geom_rect(
aes(xmin = -0.0005, xmax = 0.0005, ymin = 18.5, ymax = 19.5),
color = "white",
fill = "white"
) +
stat_bin(
aes(label = after_stat(if_else (condition = count>20, as.character(count), ""))),
bins= as.numeric(numberOfBins),
position=position_stack(vjust=0.1),
pad=TRUE,
geom = "text",
color = "black",
size = 3,
y = 19
)
#print(plot)
# Dynamic variable name for the plot
plot_name <- paste0("plot_", i - 1) # Subtracting 1 to start numbering from 1
assign(plot_name, plot)
# Add the plot to the list
plot_list[[i - 1]] <<- get(plot_name)
#rm(plot)
})
}
#print(plot_list[[1]])
#save the plotlist for later on
saveRDS(plot_list, "quarto_outputs/plotlist.rds")Generate multi-panel histogram figure 4A-L
This code compiles all 12 histograms generated above into one figure.
Show the code
plot_list <- readRDS("quarto_outputs/plotlist.rds")
patchwork <- plot_list[[1]] + xlab(NULL) +
guides(x = "none") +
plot_list[[2]] + xlab(NULL) + ylab(NULL) +
guides(x = "none", y = "none") +
plot_list[[3]] + xlab(NULL) + ylab(NULL) +
guides(x = "none", y = "none") +
plot_list[[4]] + xlab(NULL) + ylab(NULL) +
guides(x = "none", y = "none") +
plot_list[[5]] + xlab(NULL) + guides(x = "none") +
plot_list[[6]] + xlab(NULL) + ylab(NULL) +
guides(x = "none", y = "none") +
plot_list[[7]] + xlab(NULL) + ylab(NULL) +
guides(x = "none", y = "none") +
plot_list[[8]] + xlab(NULL) + ylab(NULL) +
guides(x = "none", y = "none") +
plot_list[[9]] +
plot_list[[10]] + ylab(NULL) + guides(y = "none") +
plot_list[[11]] + ylab(NULL) + guides(y = "none") +
plot_list[[12]] + ylab(NULL) + guides(y = "none") +
plot_annotation(
subtitle = ' CANAPE-Neo CANAPE-paleo CANAPE-Mixed',
caption = '',
tag_levels = 'A') +
plot_layout(ncol = 4, guides = 'collect') &
theme(legend.position = "bottom", plot.tag.position = c(0.9, 0.9),
plot.tag = element_text(size = 12, hjust = 0, vjust = 0))
#print(patchwork)
ggsave("quarto_outputs/figures/Figure4_A.png", patchwork, width = 3000, height = 2400, units = "px")Load results data & generate data files for biome maps
Now we take the RWiBaLD results and split the taxa in the original specimen data from the Categorical Analysis of Neo And Paleo-Endemisim (CANAPE) paper according to these RWiBaLD categories so we can overlay them on the biome map published by Crisp et al. in 2004.
Show the code
original_paper_specimen_data_file <- "doi_10_5061_dryad_dv4qk__v20150514/Point_distribution_Australian_Phylogenetic_Diversity_Acacia.csv"
original_paper_specimen_data <- read.table(original_paper_specimen_data_file, header=T, sep=",")
#View(original_paper_specimen_data)
#View(rwibald_results_all_with_range)
# Summarize the data in the rwibaled_type column
summary_df <- rwibald_results_all_with_range %>%
group_by(rwibald_type) %>%
summarise(count = n()) # Count of each rwibaled_type
# Create a table using gt
summary_table <- summary_df %>%
gt() %>%
tab_header(
title = "Summary of rwibaled_type",
subtitle = "Count of each type"
)
# Print the table
summary_table
# Extract the 'NAME' values where 'rwibald_type' is 'meso-endemic'
meso_names <- rwibald_results_all_with_range[rwibald_results_all_with_range$rwibald_type == "meso-endemic", "NAME"]
# Filter the 'original_paper_specimen_data' dataframe
meso_taxa <- original_paper_specimen_data[original_paper_specimen_data$Species %in% meso_names, ]
# Write data to file
write.csv(meso_taxa, "quarto_outputs/Meso_endemics.csv" ,row.names=FALSE)
# Extract the 'NAME' values where 'rwibald_type' is 'neo-endemic'
neo_names <- rwibald_results_all_with_range[rwibald_results_all_with_range$rwibald_type == "neo-endemic", "NAME"]
# Filter the 'original_paper_specimen_data' dataframe
neo_taxa <- original_paper_specimen_data[original_paper_specimen_data$Species %in% neo_names, ]
# Write data to file
write.csv(neo_taxa, "quarto_outputs/Neo_endemics.csv" ,row.names=FALSE)
# Extract the 'NAME' values where 'rwibald_type' is 'paleo-endemic'
paleo_names <- rwibald_results_all_with_range[rwibald_results_all_with_range$rwibald_type == "paleo-endemic", "NAME"]
# Filter the 'original_paper_specimen_data' dataframe
paleo_taxa <- original_paper_specimen_data[original_paper_specimen_data$Species %in% paleo_names, ]
# Write data to file
write.csv(paleo_taxa, "quarto_outputs/Paleo_endemics.csv" ,row.names=FALSE)| Summary of rwibaled_type | |
|---|---|
| Count of each type | |
| rwibald_type | count |
| meso-endemic | 59 |
| neo-endemic | 55 |
| other | 880 |
| paleo-endemic | 21 |
Two tailed relative phylogenetic diversity (RPD) & CANAPE functions
This is the function for calculating Categorical Analysis of Neo- and Paleo-endemism (CANAPE) from ranked P scores of Biodiverse analysis.
Show the code
#Standard 2 tailed test for RPD
significance_fun <- function(x){
if (x >= 0.99) {
return("Very Highly Sig")
} else if (x >= 0.975){
return ("Highly Sig")
} else if (x <= 0.01){
return ("Very Sig Low")
} else if (x <= 0.025){
return ("Sig Low")
} else {
return("Not Sig")
}
}
#two pass test for RPE
# x=P_PE_WE_P, y=P_PHYLO_RPE_NULL2, z=P_PHYLO_RPE2
significance_super_fun <- function(x, y, z){
if (x > 0.95 || y > 0.95) {
if (z <= 0.025){
return ("Neo")
} else if (z >= 0.975){
return ("paleo")
} else if (x >= 0.99 || y >= 0.99){
return ("Super")
} else {
return("Mixed")
}
} else {
return("Not Sig")
}
}Load CANAPE & randomisation results
Here we load the re-created biodiverse results from the original paper, calculate CANAPE and re-generate the CANAPE map. Note: these results may differ slightly from the original paper as we re-ran the randomisation in biodiverse.
Show the code
data_dir <- "Acacia_biodiverse_exports/"
# The CANAPE results file calculated in Biodiverse directly and exported
biodiverse_canape_results_file <- paste0(data_dir, "Acacia_Rand1_CANAPE_Export.csv")
# The spatial & randomisation results files calculated in Biodiverse and exported
biodiverse_observed_data_file <- paste0(data_dir, "Acacia_SPATIAL_RESULTS_Export.csv")
biodiverse_rand_results_file <- paste0(data_dir, "Acacia_Rand1_SPATIAL_RESULTS_Export.csv")
biodiverse_canape_results <- read.table(biodiverse_canape_results_file, header=T,sep=",", check.names = FALSE )
biodiverse_observed_spatial_results <- read.table(biodiverse_observed_data_file, header=T,sep=",")
biodiverse_rand_spatial_results <- read.table(biodiverse_rand_results_file, header=T,sep=",")
biodiverse_results_concatenated <- cbind(biodiverse_observed_spatial_results, biodiverse_rand_spatial_results)
#View(biodiverse_results_concatenated)
###############################################
#Create new columns in dataframe and
#populate them using the functions above
###############################################
targets <- c("PHYLO_RPD2", "PD_P", "PE_WE_P", "PD_P_per_taxon", "PHYLO_RPE2")
for (name in targets) {
colname <- paste0("P_", name) # prepend the P_ since we want the proportions, saves some typing above
new_colname = paste0(colname, "_SIG")
trait_index <- match (colname, colnames(biodiverse_results_concatenated))
# Apply the function to every row of column with index "trait_index"
# and generate a column in the dataframe showing significant cells
if (!is.na(trait_index)) {
biodiverse_results_concatenated[[new_colname]] <- apply (biodiverse_results_concatenated[trait_index], MARGIN=c(1), significance_fun)
} else {
print (paste("Cannot find index", colname, "in data frame"))
}
}
biodiverse_results_concatenated$P_PHYLO_RPE2_CANAPE_SIG <- sapply(
1:nrow(biodiverse_results_concatenated),
function(x) significance_super_fun(
biodiverse_results_concatenated$P_PE_WE_P[x],
biodiverse_results_concatenated$P_PHYLO_RPE_NULL2[x],
biodiverse_results_concatenated$P_PHYLO_RPE2[x]
)
)
#View(biodiverse_results_concatenated)Generate CANAPE map figure 5A
Here we generate the CANAPE map, indicating cell locations from the histograms in figure 4A-L. CANAPE identifies geographic concentrations of high PE, and gives a summary classification of the type of endemism dominating in a location. The RWiBaLD histograms (figure 4A-L) identify the specific branches that contribute the most to PE in a given cell, and what type of endemism they represent.
Show the code
#loadfonts()
myFont <- choose_font(c("HelvLight", "Arial", "sans"), quiet = TRUE) #load a font if available
map_text <- "Categorical Analysis of Neo- And Paleo- Endemism"
sigplot <- "P_PHYLO_RPE2_CANAPE_SIG"
col_scheme <- c("paleo" = "royalblue1","Not Sig" = "snow2", "Neo" = "red", "Super" = "#9D00FF", "Mixed"= "#CB7FFF")
legend_order <-c("Neo","paleo", "Not Sig", "Mixed", "Super")
legend_labels <- c("Neo"="Neo","paleo"="Paleo", "Not Sig"="Not significant", "Mixed"="Mixed", "Super"="Super")
biodiverse_results_concatenated[, sigplot] <- factor(biodiverse_results_concatenated[, sigplot], levels=legend_order)
Axis_0 <- "Axis_0"
Axis_1 <- "Axis_1"
map_shape_file <- paste0("shape_files/coastline_albers.shp")
map_data <- st_read(map_shape_file)
cols_to_keep <- c("-1825000:-2975000", "75000:-2525000", "625000:-3475000","-1425000:-3225000","1775000:-3725000", "-1425000:-3475000", "-1225000:-3775000", "-1425000:-3275000", "1225000:-1275000", "-1175000:-3275000", "-1075000:-2825000", "-1075000:-2375000")
# Initialize empty vectors for x and y
x <- c()
y <- c()
# Split each element of cols_to_keep and append to x and y for plotting on the map
for (val in cols_to_keep) {
parts <- strsplit(val, ":")[[1]]
x <- c(x, as.numeric(parts[1]))
y <- c(y, as.numeric(parts[2]))
}
# Print the result
#print(x)
#print(y)
labels <- c("A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L")
# Create data frame
histograms <- data.frame(x = x, y = y, label = labels)
# Plot the sf object with ggplot2, coloring by the BIOME column
map_plot_CANAPE <- ggplot() +
geom_tile(data=biodiverse_results_concatenated, aes_string(x=Axis_0, y=Axis_1, fill=sigplot)) +
geom_sf(data = map_data, colour = "grey77" , fill="transparent") +
scale_fill_manual(values = col_scheme, labels=legend_labels, name="CANAPE", guide = guide_legend(direction = "horizontal", title.position = "bottom", title.hjust=0.5, title.vjust=0.5, label.position="bottom", label.hjust = 0.5, label.vjust = 0.1, lineheight=0.5))+
geom_text_repel(data=histograms, aes(x = x, y = y, label = labels), fontface = "bold", size = 2.5, nudge_x = c(-200000, 0, 0, -100000, 250000, -300000, 200000, 100000, 200000, 200000, 200000, 200000), nudge_y = c(200000, 250000, 350000, 300000, -100000, 0, -200000, 200000, 200000, 200000, 200000, 200000)) +
annotate("rect", xmin = -750000, xmax = -250000, ymin = -4500000, ymax = -4550000, fill = "black", colour = "black", alpha = 1, linewidth = 0.1) +
annotate("rect", xmin = -250000, xmax = 250000, ymin = -4500000, ymax = -4550000, fill = "white", colour = "black", alpha = 1, linewidth = 0.1) +
annotate("text", label = "0", x = -750000, y = -4650000, size=rel(2), face = 'plain', family = myFont) +
annotate("text", label = "500", x = -250000, y = -4650000, size=rel(2), face = 'plain', family = myFont) +
annotate("text", label = "1000", x = 250000, y = -4650000, size=rel(2), face = 'plain', family = myFont) +
annotate("text", label = "km", x = 500000, y = -4650000, size=rel(2), face = 'plain', family = myFont) +
theme(text = element_text(family = myFont),
strip.background = element_blank(),
title = element_text(colour = 'black', angle = 0, size=rel(1), face = 'plain', family = myFont),
axis.line=element_blank(),axis.text.x=element_blank(),
axis.text.y=element_blank(),axis.ticks=element_blank(),
axis.title.x=element_blank(), axis.title.y=element_blank(),
legend.position="none",
legend.direction='horizontal',
legend.text = element_text(colour = 'black', angle = 0, size=rel(1), face = 'plain', family = myFont),
panel.grid = element_blank(),
panel.background=element_blank(),#element_rect(colour = "black", fill="white", size = 1),
panel.border = element_blank(),
plot.background=element_blank(),#element_rect(colour = "black", fill="white", size = 1),
plot.margin=unit(c(0,0,0,0),"line"))
print(map_plot_CANAPE)Show the code
Reading layer `coastline_albers' from data source
`C:\E\gitRepos\rwibald\shape_files\coastline_albers.shp' using driver `ESRI Shapefile'
Simple feature collection with 44 features and 4 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: -1887741 ymin: -4840771 xmax: 2121462 ymax: -1029671
Projected CRS: GDA94 / Australian Albers
Generate biome map with neo-endemics figure 5B
Here we show the specimen data for all of the neo-endemic taxa categorised by RWiBaLD overlaid on the biome map published by Crisp et al. in 2004.
Show the code
#loadfonts()
#myFont <- choose_font(c("HelvLight", "Arial", "sans"), quiet = TRUE) #load a font if available
col_scheme <- c("aseasonal wet" = "cornflowerblue",
"eremean" = "lightyellow1",
"monsoonal tropics" = "powderblue",
"southeastern temperate" = "moccasin",
"southwestern temperate"= "#fbb4ae")
legend_order <-c("aseasonal wet",
"eremean",
"monsoonal tropics",
"southeastern temperate",
"southwestern temperate")
legend_labels <- c("aseasonal wet" = "aseasonal wet",
"eremean" = "eremean",
"monsoonal tropics" = "monsoonal tropics",
"southeastern temperate"="southeastern temperate",
"southwestern temperate"="southwestern temperate")
map_shape_file1 <- paste0("shape_files/coastline_albers.shp")
map_data <- st_read(map_shape_file1)
#geom_sf(data = map_data, colour = "grey77" , fill="transparent") +
map_shape_file <- paste0("shape_files/biomes_crisp_3577/biomes_crisp_3577.shp")
# Read the shapefile as an sf object
biomes_sf <- st_read(map_shape_file)
csv_file <- paste0("quarto_outputs/Neo_endemics.csv")
# Read the CSV file into a data frame
csv_data <- read.csv(csv_file)
# Convert data frame to sf object
csv_sf <- st_as_sf(csv_data, coords = c("x_metres_EPSG_3577_Albers_Equal_Area", "y_metres_EPSG_3577_Albers_Equal_Area"), crs = st_crs(biomes_sf))
# Plot the sf object with ggplot2, coloring by the BIOME column
map_plot_Neo <- ggplot() +
geom_sf(data = biomes_sf, aes(fill = BIOME), colour = "grey77") +
geom_sf(data = map_data, colour = "grey77" , fill="transparent") +
geom_sf(data = csv_sf, fill= "red", color = "red4", shape = 21) +
scale_fill_manual(values = col_scheme, labels=legend_labels, name="Biomes", guide = guide_legend(direction = "horizontal", title.position = "bottom", title.hjust=0.5, title.vjust=0.5, label.position="bottom", label.hjust = 0.5, label.vjust = 0.1, lineheight=0.5))+
theme(text = element_text(family = myFont),
strip.background = element_blank(),
title = element_text(colour = 'black', angle = 0, size=rel(1), face = 'plain', family = myFont),
axis.line=element_blank(),axis.text.x=element_blank(),
axis.text.y=element_blank(),axis.ticks=element_blank(),
axis.title.x=element_blank(), axis.title.y=element_blank(),
legend.position="none",
legend.direction='horizontal',
legend.text = element_text(colour = 'black', angle = 0, size=rel(1), face = 'plain', family = myFont),
legend.key = element_rect(color = "black", size = 0),
panel.grid = element_blank(),
panel.background=element_blank(),#element_rect(colour = "black", fill="white", size = 1),
panel.border = element_blank(),
plot.background=element_blank(),#element_rect(colour = "black", fill="white", size = 1),
plot.margin=unit(c(0,0,0,0),"line"))
print(map_plot_Neo)Show the code
Reading layer `coastline_albers' from data source
`C:\E\gitRepos\rwibald\shape_files\coastline_albers.shp' using driver `ESRI Shapefile'
Simple feature collection with 44 features and 4 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: -1887741 ymin: -4840771 xmax: 2121462 ymax: -1029671
Projected CRS: GDA94 / Australian Albers
Reading layer `biomes_crisp_3577' from data source
`C:\E\gitRepos\rwibald\shape_files\biomes_crisp_3577\biomes_crisp_3577.shp'
using driver `ESRI Shapefile'
Simple feature collection with 5 features and 3 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -1887414 ymin: -4840588 xmax: 2121442 ymax: -1087816
Projected CRS: GDA94 / Australian Albers
Generate biome map with meso-endemics figure 5C
Here we show the specimen data for all of the meso-endemic taxa categorised by RWiBaLD overlaid on the biome map published by Crisp et al. in 2004.
Show the code
#loadfonts()
#myFont <- choose_font(c("HelvLight", "Arial", "sans"), quiet = TRUE) #load a font if available
map_text <- "Categorical Analysis of Neo- And Paleo- Endemism"
col_scheme <- c("aseasonal wet" = "cornflowerblue",
"eremean" = "lightyellow1",
"monsoonal tropics" = "powderblue",
"southeastern temperate" = "moccasin",
"southwestern temperate"= "#fbb4ae")
legend_order <-c("aseasonal wet",
"eremean",
"monsoonal tropics",
"southeastern temperate",
"southwestern temperate")
legend_labels <- c("aseasonal wet" = "aseasonal wet",
"eremean" = "eremean",
"monsoonal tropics" = "monsoonal tropics",
"southeastern temperate"="southeastern temperate",
"southwestern temperate"="southwestern temperate")
map_shape_file1 <- paste0("shape_files/coastline_albers.shp")
map_data <- st_read(map_shape_file1)
#geom_sf(data = map_data, colour = "grey77" , fill="transparent") +
map_shape_file <- paste0("shape_files/biomes_crisp_3577/biomes_crisp_3577.shp")
# Read the shapefile as an sf object
biomes_sf <- st_read(map_shape_file)
csv_file <- paste0("quarto_outputs/Meso_endemics.csv")
# Read the CSV file into a data frame
csv_data <- read.csv(csv_file)
# Convert data frame to sf object
csv_sf <- st_as_sf(csv_data, coords = c("x_metres_EPSG_3577_Albers_Equal_Area", "y_metres_EPSG_3577_Albers_Equal_Area"), crs = st_crs(biomes_sf))
# Plot the sf object with ggplot2, coloring by the BIOME column
map_plot_Meso <- ggplot() +
geom_sf(data = biomes_sf, aes(fill = BIOME), color = "grey77") +
geom_sf(data = map_data, colour = "grey77" , fill="transparent") +
geom_sf(data = csv_sf, fill= "forestgreen", color = "darkgreen", shape = 21) +
scale_fill_manual(values = col_scheme, labels=legend_labels, name="Biomes", guide = guide_legend(direction = "horizontal", title.position = "bottom", title.hjust=0.5, title.vjust=0.5, label.position="bottom", label.hjust = 0.5, label.vjust = 0.1, lineheight=0.5))+
theme(text = element_text(family = myFont),
strip.background = element_blank(),
title = element_text(colour = 'black', angle = 0, size=rel(1), face = 'plain', family = myFont),
axis.line=element_blank(),axis.text.x=element_blank(),
axis.text.y=element_blank(),axis.ticks=element_blank(),
axis.title.x=element_blank(), axis.title.y=element_blank(),
legend.position="none",
legend.direction='horizontal',
legend.text = element_text(colour = 'black', angle = 0, size=rel(1), face = 'plain', family = myFont),
legend.key = element_rect(color = "black", size = 0),
panel.grid = element_blank(),
panel.background=element_blank(),#element_rect(colour = "black", fill="white", size = 1),
panel.border = element_blank(),
plot.background=element_blank(),#element_rect(colour = "black", fill="white", size = 1),
plot.margin=unit(c(0,0,0,0),"line"))
print(map_plot_Meso)Show the code
Reading layer `coastline_albers' from data source
`C:\E\gitRepos\rwibald\shape_files\coastline_albers.shp' using driver `ESRI Shapefile'
Simple feature collection with 44 features and 4 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: -1887741 ymin: -4840771 xmax: 2121462 ymax: -1029671
Projected CRS: GDA94 / Australian Albers
Reading layer `biomes_crisp_3577' from data source
`C:\E\gitRepos\rwibald\shape_files\biomes_crisp_3577\biomes_crisp_3577.shp'
using driver `ESRI Shapefile'
Simple feature collection with 5 features and 3 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -1887414 ymin: -4840588 xmax: 2121442 ymax: -1087816
Projected CRS: GDA94 / Australian Albers
Generate biome map with paleo-endemics figure 5D
Here we show the specimen data for all of the paleo-endemic taxa categorised by RWiBaLD overlaid on the biome map published by Crisp et al. in 2004.
Show the code
#loadfonts()
#myFont <- choose_font(c("HelvLight", "Arial", "sans"), quiet = TRUE) #load a font if available
map_text <- "Categorical Analysis of Neo- And Paleo- Endemism"
col_scheme <- c("aseasonal wet" = "cornflowerblue",
"eremean" = "lightyellow1",
"monsoonal tropics" = "powderblue",
"southeastern temperate" = "moccasin",
"southwestern temperate"= "#fbb4ae")
legend_order <-c("aseasonal wet",
"eremean",
"monsoonal tropics",
"southeastern temperate",
"southwestern temperate")
legend_labels <- c("aseasonal wet" = "aseasonal wet",
"eremean" = "eremean",
"monsoonal tropics" = "monsoonal tropics",
"southeastern temperate"="southeastern temperate",
"southwestern temperate"="southwestern temperate")
map_shape_file1 <- paste0("shape_files/coastline_albers.shp")
map_data <- st_read(map_shape_file1)
#geom_sf(data = map_data, colour = "grey77" , fill="transparent") +
map_shape_file <- paste0("shape_files/biomes_crisp_3577/biomes_crisp_3577.shp")
# Read the shapefile as an sf object
biomes_sf <- st_read(map_shape_file)
csv_file <- paste0("quarto_outputs/Paleo_endemics.csv")
# Read the CSV file into a data frame
csv_data <- read.csv(csv_file)
# Convert data frame to sf object
csv_sf <- st_as_sf(csv_data, coords = c("x_metres_EPSG_3577_Albers_Equal_Area", "y_metres_EPSG_3577_Albers_Equal_Area"), crs = st_crs(biomes_sf))
# Plot the sf object with ggplot2, coloring by the BIOME column
map_plot_Paleo <- ggplot() +
geom_sf(data = biomes_sf, aes(fill = BIOME), color = "grey77") +
geom_sf(data = map_data, colour = "grey77" , fill="transparent") +
geom_sf(data = csv_sf, fill= "royalblue3", color = "darkblue", shape = 21) +
scale_fill_manual(values = col_scheme, labels=legend_labels, name="Biomes", guide = guide_legend(direction = "horizontal", title.position = "bottom", title.hjust=0.5, title.vjust=0.5, label.position="bottom", label.hjust = 0.5, label.vjust = 0.1, lineheight=0.5))+
theme(text = element_text(family = myFont),
strip.background = element_blank(),
title = element_text(colour = 'black', angle = 0, size=rel(1), face = 'plain', family = myFont),
axis.line=element_blank(),axis.text.x=element_blank(),
axis.text.y=element_blank(),axis.ticks=element_blank(),
axis.title.x=element_blank(), axis.title.y=element_blank(),
legend.position="none",
legend.direction='horizontal',
legend.text = element_text(colour = 'black', angle = 0, size=rel(0.5), face = 'plain', family = myFont),
legend.key = element_rect(color = "black", size = 0),
panel.grid = element_blank(),
panel.background=element_blank(),#element_rect(colour = "black", fill="white", size = 1),
panel.border = element_blank(),
plot.background=element_blank(),#element_rect(colour = "black", fill="white", size = 1),
plot.margin=unit(c(0,0,0,0),"line"))
print(map_plot_Paleo)Show the code
Reading layer `coastline_albers' from data source
`C:\E\gitRepos\rwibald\shape_files\coastline_albers.shp' using driver `ESRI Shapefile'
Simple feature collection with 44 features and 4 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: -1887741 ymin: -4840771 xmax: 2121462 ymax: -1029671
Projected CRS: GDA94 / Australian Albers
Reading layer `biomes_crisp_3577' from data source
`C:\E\gitRepos\rwibald\shape_files\biomes_crisp_3577\biomes_crisp_3577.shp'
using driver `ESRI Shapefile'
Simple feature collection with 5 features and 3 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -1887414 ymin: -4840588 xmax: 2121442 ymax: -1087816
Projected CRS: GDA94 / Australian Albers
Generate 4 up map figure 5ABCD
Here we compile the maps into one figure 5A-D
Show the code
patchwork_map <- map_plot_CANAPE +
map_plot_Neo +
map_plot_Meso +
map_plot_Paleo
patchwork_map <- patchwork_map +
plot_annotation(subtitle = '',
caption = '',
tag_levels = 'A') +
plot_layout(ncol = 2, guides = 'collect') &
theme(legend.position = "bottom",
legend.title=element_text(size=rel(0.6)),
legend.text=element_text(size=rel(0.5)),
legend.title.position = "bottom",
legend.label.position= "bottom",
legend.key = element_rect(color = "black", size = 0.5),
legend.key.height = unit(0.5, "cm"),
legend.key.width = unit(0.5, "cm"),
plot.tag.position = c(0.9, 0.9),
plot.tag = element_text(size = 12, hjust = 0, vjust = 0))
#print(patchwork_map)
ggsave('quarto_outputs/figures/Figure5_ABCD.png', patchwork_map, width = 3000, height = 2600, units = "px")Table of RWiBaLD results supplementary table 1
RWiBaLD results for each branch on the phylogeny of Australian Acacia.
Show the code
RWiBaLD_results_csv <- paste0("quarto_outputs/Acacia_RWiBaLD_results_all_with_range.csv")
rwibald_results_all_with_range <- read.table(RWiBaLD_results_csv, header=T,sep=",")
#View(rwibald_results_all_with_range)
#colnames(rwibald_results_all_with_range)
gt_table <- rwibald_results_all_with_range %>%
select("NAME", "branch_length_comparison_tree", "branch_length_observed_tree",
"rwibald_score", "rwibald_type", "range_cell_count")
# print table using gt
rwibald_results_all_with_range_table <- gt(gt_table) %>%
tab_options(
table.width = pct(100),
table.layout = "auto",
table.align = "left",
table.margin.left = px(5),
table.margin.right = px(5),
table.font.size = px(8),
column_labels.font.size = px(10),
heading.align = "center",
heading.title.font.size = px(12),
quarto.use_bootstrap = TRUE
) %>%
opt_row_striping() %>%
tab_header(
title = md("RWiBaLD Results")
) %>%
tab_style(
style = cell_text(align = "center"),
locations = cells_body()
)
rwibald_results_all_with_range_tableRWiBaLD Results |
|||||
|---|---|---|---|---|---|
| NAME | branch_length_comparison_tree | branch_length_observed_tree | rwibald_score | rwibald_type | range_cell_count |
| 1___ | 8.777963e-05 | 2.234812e-04 | 1.357015e-04 | other | 103 |
| 10___ | 8.951784e-05 | 3.583689e-05 | -5.368095e-05 | other | 101 |
| 100___ | 4.109683e-05 | 7.239508e-06 | -3.385732e-05 | other | 220 |
| 101___ | 2.366833e-05 | 3.361011e-05 | 9.941780e-06 | other | 382 |
| 102___ | 2.318283e-05 | 1.884262e-05 | -4.340207e-06 | other | 390 |
| 103___ | 2.137424e-05 | 4.697007e-05 | 2.559583e-05 | other | 423 |
| 104___ | 2.318283e-04 | 9.137027e-04 | 6.818744e-04 | other | 39 |
| 105___ | 6.027535e-04 | 1.624921e-03 | 1.022168e-03 | other | 15 |
| 106___ | 1.532424e-04 | 1.057493e-04 | -4.749313e-05 | other | 59 |
| 107___ | 9.935497e-05 | 3.545809e-04 | 2.552259e-04 | other | 91 |
| 108___ | 9.827502e-05 | 3.429999e-05 | -6.397503e-05 | other | 92 |
| 109___ | 8.449815e-05 | 1.130098e-04 | 2.851166e-05 | other | 107 |
| 11___ | 8.777963e-05 | 2.896118e-05 | -5.881846e-05 | other | 103 |
| 110___ | 1.891486e-05 | 9.029480e-06 | -9.885378e-06 | other | 478 |
| 111___ | 7.534419e-04 | 1.343083e-03 | 5.896411e-04 | other | 12 |
| 112___ | 1.614518e-04 | 4.342113e-05 | -1.180307e-04 | other | 56 |
| 113___ | 1.051314e-04 | 1.208033e-04 | 1.567189e-05 | other | 86 |
| 114___ | 8.777963e-05 | 6.943275e-09 | -8.777269e-05 | other | 103 |
| 115___ | 7.862002e-05 | 9.885975e-06 | -6.873404e-05 | other | 115 |
| 116___ | 7.175637e-05 | 1.346953e-05 | -5.828684e-05 | other | 126 |
| 117___ | 7.008761e-05 | 4.083340e-05 | -2.925421e-05 | other | 129 |
| 118___ | 7.008761e-05 | 5.616836e-05 | -1.391925e-05 | other | 129 |
| 119___ | 6.954848e-05 | 1.778353e-05 | -5.176495e-05 | other | 130 |
| 12___ | 1.335495e-05 | 1.097784e-05 | -2.377112e-06 | other | 677 |
| 120___ | 1.597403e-05 | 7.837726e-06 | -8.136307e-06 | other | 566 |
| 121___ | 1.504376e-05 | 1.467145e-05 | -3.723155e-07 | other | 601 |
| 122___ | 9.881205e-06 | 4.100078e-06 | -5.781126e-06 | other | 915 |
| 123___ | 9.859654e-06 | 1.414163e-06 | -8.445490e-06 | other | 917 |
| 124___ | 3.117690e-04 | 3.328489e-04 | 2.107982e-05 | other | 29 |
| 125___ | 2.511473e-04 | 3.887985e-04 | 1.376512e-04 | other | 36 |
| 126___ | 2.054841e-04 | 4.573766e-05 | -1.597465e-04 | other | 44 |
| 127___ | 2.825407e-04 | 6.909660e-05 | -2.134441e-04 | other | 32 |
| 128___ | 2.825407e-04 | 2.411957e-04 | -4.134496e-05 | other | 32 |
| 129___ | 5.022946e-04 | 2.914361e-04 | -2.108585e-04 | other | 18 |
| 13___ | 1.310334e-05 | 5.646846e-06 | -7.456490e-06 | other | 690 |
| 130___ | 1.130163e-04 | 3.901460e-05 | -7.400168e-05 | other | 80 |
| 131___ | 1.004589e-04 | 4.157142e-05 | -5.888750e-05 | other | 90 |
| 132___ | 7.862002e-05 | 1.278658e-04 | 4.924578e-05 | other | 115 |
| 133___ | 7.662121e-05 | 3.954025e-05 | -3.708095e-05 | other | 118 |
| 134___ | 3.863804e-05 | 1.794236e-06 | -3.684381e-05 | other | 234 |
| 135___ | 4.346780e-05 | 4.264787e-05 | -8.199310e-07 | other | 208 |
| 136___ | 6.551668e-05 | 4.443585e-05 | -2.108084e-05 | other | 138 |
| 137___ | 2.807858e-05 | 5.238707e-05 | 2.430849e-05 | other | 322 |
| 138___ | 2.539692e-05 | 7.418464e-05 | 4.878772e-05 | other | 356 |
| 139___ | 4.758580e-04 | 6.521900e-05 | -4.106390e-04 | other | 19 |
| 14___ | 1.145919e-05 | 4.368943e-06 | -7.090249e-06 | other | 789 |
| 140___ | 3.767209e-04 | 2.539890e-04 | -1.227320e-04 | other | 24 |
| 141___ | 3.477424e-04 | 5.308590e-04 | 1.831166e-04 | other | 26 |
| 142___ | 2.916549e-04 | 4.352484e-04 | 1.435935e-04 | other | 31 |
| 143___ | 2.054841e-04 | 2.656276e-05 | -1.789214e-04 | other | 44 |
| 144___ | 1.506884e-04 | 1.191929e-08 | -1.506765e-04 | other | 60 |
| 145___ | 1.238535e-04 | 5.947281e-05 | -6.438065e-05 | other | 73 |
| 146___ | 1.221798e-04 | 3.874051e-05 | -8.343925e-05 | other | 74 |
| 147___ | 8.145317e-05 | 9.120355e-05 | 9.750379e-06 | other | 111 |
| 148___ | 1.936039e-05 | 8.736721e-06 | -1.062367e-05 | other | 467 |
| 149___ | 1.361642e-05 | 7.233721e-06 | -6.382699e-06 | other | 664 |
| 15___ | 1.090628e-05 | 2.498268e-05 | 1.407641e-05 | other | 829 |
| 150___ | 1.302781e-05 | 3.732148e-06 | -9.295665e-06 | other | 694 |
| 151___ | 3.931001e-04 | 1.185664e-04 | -2.745337e-04 | other | 23 |
| 152___ | 2.318283e-04 | 6.839497e-05 | -1.634333e-04 | other | 39 |
| 153___ | 1.586193e-04 | 1.557460e-04 | -2.873300e-06 | other | 57 |
| 154___ | 7.008761e-05 | 6.309607e-06 | -6.377801e-05 | other | 129 |
| 155___ | 6.551668e-05 | 1.174379e-05 | -5.377289e-05 | other | 138 |
| 156___ | 6.504534e-05 | 4.422731e-05 | -2.081803e-05 | other | 139 |
| 157___ | 3.631045e-05 | 1.614817e-05 | -2.016228e-05 | other | 249 |
| 158___ | 5.758791e-05 | 5.237361e-05 | -5.214300e-06 | other | 157 |
| 159___ | 4.589494e-05 | 1.856155e-05 | -2.733338e-05 | other | 197 |
| 16___ | 6.901757e-05 | 9.388402e-05 | 2.486645e-05 | other | 131 |
| 160___ | 4.000576e-05 | 2.116269e-05 | -1.884307e-05 | other | 226 |
| 161___ | 2.620667e-05 | 2.072920e-09 | -2.620460e-05 | other | 345 |
| 162___ | 1.369894e-04 | 5.770269e-05 | -7.928674e-05 | other | 66 |
| 163___ | 1.027421e-04 | 1.694995e-05 | -8.579212e-05 | other | 88 |
| 164___ | 8.001152e-05 | 3.946034e-05 | -4.055118e-05 | other | 113 |
| 165___ | 5.318413e-05 | 4.206808e-09 | -5.317992e-05 | other | 170 |
| 166___ | 4.224908e-05 | 3.435192e-06 | -3.881388e-05 | other | 214 |
| 167___ | 9.132629e-05 | 2.496945e-05 | -6.635684e-05 | other | 99 |
| 168___ | 2.825407e-04 | 4.848574e-05 | -2.340550e-04 | other | 32 |
| 169___ | 1.674315e-04 | 2.781283e-05 | -1.396187e-04 | other | 54 |
| 17___ | 6.192673e-05 | 8.906534e-05 | 2.713862e-05 | other | 146 |
| 170___ | 1.205507e-04 | 3.798806e-05 | -8.256264e-05 | other | 75 |
| 171___ | 1.205507e-04 | 5.692448e-05 | -6.362621e-05 | other | 75 |
| 172___ | 1.205507e-04 | 1.042297e-04 | -1.632098e-05 | other | 75 |
| 173___ | 1.174195e-04 | 4.595375e-05 | -7.146576e-05 | other | 77 |
| 174___ | 1.063683e-04 | 1.635680e-06 | -1.047326e-04 | other | 85 |
| 175___ | 8.777963e-05 | 6.665401e-06 | -8.111423e-05 | other | 103 |
| 176___ | 5.079383e-05 | 4.017738e-09 | -5.078982e-05 | other | 178 |
| 177___ | 4.967749e-05 | 1.645418e-05 | -3.322331e-05 | other | 182 |
| 178___ | 3.631045e-05 | 2.872118e-09 | -3.630758e-05 | other | 249 |
| 179___ | 2.816605e-05 | 1.232424e-05 | -1.584181e-05 | other | 321 |
| 18___ | 2.379290e-04 | 2.770606e-05 | -2.102229e-04 | other | 38 |
| 180___ | 2.226922e-05 | 2.067711e-06 | -2.020151e-05 | other | 406 |
| 181___ | 1.583415e-05 | 9.804553e-06 | -6.029602e-06 | other | 571 |
| 182___ | 1.382462e-05 | 2.249130e-06 | -1.157549e-05 | other | 654 |
| 183___ | 5.795707e-05 | 6.485613e-05 | 6.899068e-06 | other | 156 |
| 184___ | 5.413953e-05 | 3.121126e-05 | -2.292827e-05 | other | 167 |
| 185___ | 8.371576e-05 | 6.061842e-05 | -2.309734e-05 | other | 108 |
| 186___ | 8.072591e-05 | 6.385333e-09 | -8.071953e-05 | other | 112 |
| 187___ | 7.930967e-05 | 2.326218e-05 | -5.604749e-05 | other | 114 |
| 188___ | 7.862002e-05 | 3.879562e-05 | -3.982440e-05 | other | 115 |
| 189___ | 6.027535e-04 | 6.557666e-04 | 5.301308e-05 | other | 15 |
| 19___ | 9.827502e-05 | 6.119933e-05 | -3.707570e-05 | other | 92 |
| 190___ | 2.659207e-04 | 1.346622e-04 | -1.312585e-04 | other | 34 |
| 191___ | 2.443595e-04 | 1.932858e-08 | -2.443402e-04 | other | 37 |
| 192___ | 7.727609e-05 | 2.609021e-05 | -5.118588e-05 | other | 117 |
| 193___ | 6.747241e-05 | 4.129034e-05 | -2.618207e-05 | other | 134 |
| 194___ | 5.318413e-04 | 7.492230e-05 | -4.569190e-04 | other | 17 |
| 195___ | 1.923681e-04 | 1.878672e-04 | -4.500934e-06 | other | 47 |
| 196___ | 6.901757e-05 | 4.168372e-05 | -2.733386e-05 | other | 131 |
| 197___ | 6.551668e-05 | 6.230231e-06 | -5.928645e-05 | other | 138 |
| 198___ | 6.551668e-05 | 2.481117e-05 | -4.070552e-05 | other | 138 |
| 199___ | 6.192673e-05 | 3.577419e-05 | -2.615254e-05 | other | 146 |
| 2___ | 8.529530e-05 | 1.408452e-04 | 5.554991e-05 | other | 106 |
| 20___ | 4.367779e-05 | 4.996096e-05 | 6.283174e-06 | other | 207 |
| 200___ | 5.686354e-05 | 4.728088e-05 | -9.582659e-06 | other | 159 |
| 201___ | 3.847363e-05 | 1.174597e-05 | -2.672766e-05 | other | 235 |
| 202___ | 3.631045e-05 | 4.837139e-06 | -3.147331e-05 | other | 249 |
| 203___ | 3.490850e-05 | 3.357560e-06 | -3.155094e-05 | other | 259 |
| 204___ | 4.520651e-04 | 8.089279e-05 | -3.711723e-04 | other | 20 |
| 205___ | 2.009178e-04 | 4.073891e-05 | -1.601789e-04 | other | 45 |
| 206___ | 1.586193e-04 | 2.724207e-05 | -1.313773e-04 | other | 57 |
| 207___ | 4.684613e-05 | 3.858457e-06 | -4.298767e-05 | other | 193 |
| 208___ | 4.636565e-05 | 1.780143e-06 | -4.458551e-05 | other | 195 |
| 209___ | 4.018357e-05 | 3.683896e-06 | -3.649967e-05 | other | 225 |
| 21___ | 4.367779e-05 | 5.050867e-05 | 6.830880e-06 | other | 207 |
| 210___ | 3.490850e-05 | 3.157915e-06 | -3.175059e-05 | other | 259 |
| 211___ | 4.758580e-04 | 3.478897e-04 | -1.279683e-04 | other | 19 |
| 212___ | 1.004589e-03 | 1.252213e-04 | -8.793679e-04 | neo-endemic | 9 |
| 213___ | 3.767209e-04 | 2.979822e-08 | -3.766911e-04 | other | 24 |
| 214___ | 2.916549e-04 | 1.445634e-04 | -1.470915e-04 | other | 31 |
| 215___ | 2.825407e-04 | 2.282277e-05 | -2.597179e-04 | other | 32 |
| 216___ | 2.205196e-04 | 1.832734e-05 | -2.021922e-04 | other | 41 |
| 217___ | 1.772804e-04 | 1.402269e-08 | -1.772664e-04 | other | 51 |
| 218___ | 1.643873e-04 | 1.300286e-08 | -1.643743e-04 | other | 55 |
| 219___ | 1.558845e-04 | 1.233030e-08 | -1.558722e-04 | other | 58 |
| 22___ | 3.183557e-05 | 2.970162e-05 | -2.133952e-06 | other | 284 |
| 220___ | 1.255736e-04 | 9.932740e-09 | -1.255637e-04 | other | 72 |
| 221___ | 3.437758e-05 | 9.878504e-06 | -2.449907e-05 | other | 263 |
| 222___ | 6.901757e-05 | 5.459216e-09 | -6.901212e-05 | other | 131 |
| 223___ | 6.412271e-05 | 1.364680e-05 | -5.047591e-05 | other | 141 |
| 224___ | 2.102628e-04 | 1.663157e-08 | -2.102462e-04 | other | 43 |
| 225___ | 3.477424e-04 | 5.693195e-05 | -2.908104e-04 | other | 26 |
| 226___ | 2.511473e-04 | 4.780370e-05 | -2.033436e-04 | other | 36 |
| 227___ | 2.511473e-04 | 1.986548e-08 | -2.511274e-04 | other | 36 |
| 228___ | 2.443595e-04 | 1.932858e-08 | -2.443402e-04 | other | 37 |
| 229___ | 2.443595e-04 | 5.971620e-05 | -1.846433e-04 | other | 37 |
| 23___ | 3.064848e-05 | 1.226722e-04 | 9.202372e-05 | other | 295 |
| 230___ | 5.349883e-05 | 4.231700e-09 | -5.349460e-05 | other | 169 |
| 231___ | 5.051007e-05 | 1.239962e-05 | -3.811045e-05 | other | 179 |
| 232___ | 4.834921e-05 | 3.824371e-09 | -4.834539e-05 | other | 187 |
| 233___ | 1.063683e-04 | 8.413615e-09 | -1.063598e-04 | other | 85 |
| 234___ | 1.369894e-04 | 3.542795e-05 | -1.015615e-04 | other | 66 |
| 235___ | 1.329603e-04 | 4.690909e-05 | -8.605124e-05 | other | 68 |
| 236___ | 1.883605e-04 | 1.489911e-08 | -1.883456e-04 | other | 48 |
| 237___ | 2.916549e-04 | 7.235236e-05 | -2.193026e-04 | other | 31 |
| 238___ | 4.109683e-04 | 4.873219e-05 | -3.622361e-04 | other | 22 |
| 239___ | 1.808260e-04 | 1.638970e-05 | -1.644363e-04 | other | 50 |
| 24___ | 2.147578e-05 | 9.431207e-06 | -1.204457e-05 | other | 421 |
| 240___ | 9.935497e-05 | 8.749122e-06 | -9.060585e-05 | other | 91 |
| 241___ | 9.721830e-05 | 7.689864e-09 | -9.721061e-05 | other | 93 |
| 242___ | 5.650814e-04 | 1.624080e-04 | -4.026734e-04 | other | 16 |
| 243___ | 4.809203e-05 | 1.864923e-05 | -2.944280e-05 | other | 188 |
| 244___ | 4.367779e-05 | 7.142586e-06 | -3.653520e-05 | other | 207 |
| 245___ | 8.610764e-05 | 6.811022e-09 | -8.610083e-05 | other | 105 |
| 246___ | 8.001152e-05 | 6.669451e-06 | -7.334207e-05 | other | 113 |
| 247___ | 5.226186e-05 | 4.377641e-06 | -4.788422e-05 | other | 173 |
| 248___ | 2.790525e-05 | 2.207276e-09 | -2.790305e-05 | other | 324 |
| 249___ | 2.739789e-05 | 2.475063e-06 | -2.492282e-05 | other | 330 |
| 25___ | 1.891486e-05 | 4.604526e-06 | -1.431033e-05 | other | 478 |
| 250___ | 2.628286e-05 | 2.078946e-09 | -2.628078e-05 | other | 344 |
| 251___ | 2.490717e-05 | 2.349888e-06 | -2.255728e-05 | other | 363 |
| 252___ | 2.477069e-05 | 1.959335e-09 | -2.476873e-05 | other | 365 |
| 253___ | 2.404602e-05 | 2.432616e-06 | -2.161340e-05 | other | 376 |
| 254___ | 2.199830e-05 | 4.456066e-06 | -1.754224e-05 | other | 411 |
| 255___ | 2.189177e-05 | 2.214796e-06 | -1.967698e-05 | other | 413 |
| 256___ | 2.142489e-05 | 1.694686e-09 | -2.142319e-05 | other | 422 |
| 257___ | 1.982742e-05 | 6.024675e-06 | -1.380274e-05 | other | 456 |
| 258___ | 1.948557e-05 | 1.541287e-09 | -1.948402e-05 | other | 464 |
| 259___ | 1.833936e-05 | 3.784195e-06 | -1.455516e-05 | other | 493 |
| 26___ | 1.848937e-05 | 4.457219e-06 | -1.403215e-05 | other | 489 |
| 260___ | 1.783294e-05 | 1.391071e-06 | -1.644187e-05 | other | 507 |
| 261___ | 1.779784e-05 | 5.150657e-06 | -1.264718e-05 | other | 508 |
| 262___ | 1.620305e-05 | 2.735927e-06 | -1.346712e-05 | other | 558 |
| 263___ | 1.569671e-05 | 2.878626e-06 | -1.281808e-05 | other | 576 |
| 264___ | 9.398443e-06 | 8.694522e-06 | -7.039213e-07 | other | 962 |
| 265___ | 8.907687e-06 | 9.213716e-07 | -7.986315e-06 | other | 1015 |
| 266___ | 8.898920e-06 | 2.122882e-05 | 1.232990e-05 | other | 1016 |
| 267___ | 1.089314e-04 | 1.554625e-04 | 4.653110e-05 | other | 83 |
| 268___ | 1.808260e-03 | 1.430315e-07 | -1.808117e-03 | neo-endemic | 5 |
| 269___ | 4.109683e-04 | 2.854363e-05 | -3.824247e-04 | other | 22 |
| 27___ | 1.231785e-05 | 1.413074e-05 | 1.812889e-06 | other | 734 |
| 270___ | 4.612909e-05 | 7.841023e-06 | -3.828807e-05 | other | 196 |
| 271___ | 4.018357e-05 | 4.209988e-06 | -3.597358e-05 | other | 225 |
| 272___ | 4.000576e-05 | 3.164413e-09 | -4.000260e-05 | other | 226 |
| 273___ | 3.913984e-05 | 1.314225e-05 | -2.599759e-05 | other | 231 |
| 274___ | 3.880387e-05 | 4.930536e-06 | -3.387334e-05 | other | 233 |
| 275___ | 3.847363e-05 | 1.730259e-05 | -2.117104e-05 | other | 235 |
| 276___ | 3.736075e-05 | 6.705231e-06 | -3.065552e-05 | other | 242 |
| 277___ | 2.897853e-05 | 7.521549e-06 | -2.145698e-05 | other | 312 |
| 278___ | 2.102628e-04 | 1.287104e-04 | -8.155248e-05 | other | 43 |
| 279___ | 8.371576e-05 | 6.621827e-09 | -8.370914e-05 | other | 108 |
| 28___ | 1.110725e-05 | 2.269639e-05 | 1.158914e-05 | other | 814 |
| 280___ | 1.506884e-03 | 6.490422e-04 | -8.578415e-04 | neo-endemic | 6 |
| 281___ | 6.458073e-04 | 2.256771e-03 | 1.610963e-03 | other | 14 |
| 282___ | 5.349883e-05 | 1.031773e-05 | -4.318110e-05 | other | 169 |
| 283___ | 3.705452e-05 | 6.575738e-06 | -3.047878e-05 | other | 244 |
| 284___ | 5.256571e-05 | 5.367677e-05 | 1.111060e-06 | other | 172 |
| 285___ | 1.412703e-04 | 8.163462e-06 | -1.331069e-04 | other | 64 |
| 286___ | 4.388982e-05 | 2.595055e-05 | -1.793927e-05 | other | 206 |
| 287___ | 1.339452e-05 | 1.474387e-05 | 1.349347e-06 | other | 675 |
| 288___ | 5.022946e-04 | 2.487536e-04 | -2.535410e-04 | other | 18 |
| 289___ | 4.264765e-05 | 2.549108e-05 | -1.715657e-05 | other | 212 |
| 29___ | 6.551668e-05 | 6.175468e-05 | -3.762008e-06 | other | 138 |
| 290___ | 1.197523e-05 | 3.172861e-06 | -8.802374e-06 | other | 755 |
| 291___ | 1.160629e-05 | 7.321023e-06 | -4.285270e-06 | other | 779 |
| 292___ | 7.410904e-05 | 5.032021e-05 | -2.378883e-05 | other | 122 |
| 293___ | 1.390970e-04 | 1.205114e-05 | -1.270458e-04 | other | 65 |
| 294___ | 6.067988e-05 | 4.193568e-05 | -1.874420e-05 | other | 149 |
| 295___ | 5.948225e-05 | 4.704982e-09 | -5.947755e-05 | other | 152 |
| 296___ | 5.615716e-05 | 4.441971e-09 | -5.615272e-05 | other | 161 |
| 297___ | 5.446568e-05 | 2.553282e-05 | -2.893286e-05 | other | 166 |
| 298___ | 4.453843e-05 | 4.152567e-05 | -3.012765e-06 | other | 203 |
| 299___ | 2.605563e-05 | 1.171474e-05 | -1.434089e-05 | other | 347 |
| 3___ | 8.449815e-05 | 2.284378e-05 | -6.165437e-05 | other | 107 |
| 30___ | 7.175637e-05 | 2.194683e-04 | 1.477120e-04 | other | 126 |
| 300___ | 5.137104e-05 | 6.357156e-05 | 1.220052e-05 | other | 176 |
| 301___ | 3.264008e-05 | 2.424527e-05 | -8.394812e-06 | other | 277 |
| 302___ | 1.205507e-04 | 9.535431e-09 | -1.205412e-04 | other | 75 |
| 303___ | 5.758791e-05 | 1.258129e-05 | -4.500663e-05 | other | 157 |
| 304___ | 4.660465e-05 | 2.054523e-05 | -2.605942e-05 | other | 194 |
| 305___ | 4.913751e-05 | 1.646566e-05 | -3.267185e-05 | other | 184 |
| 306___ | 3.117690e-04 | 3.921233e-05 | -2.725567e-04 | other | 29 |
| 307___ | 3.229037e-04 | 1.402147e-04 | -1.826889e-04 | other | 28 |
| 308___ | 1.705906e-04 | 9.908724e-05 | -7.150337e-05 | other | 53 |
| 309___ | 1.116210e-04 | 1.032886e-05 | -1.012922e-04 | other | 81 |
| 31___ | 3.616521e-05 | 6.754410e-05 | 3.137889e-05 | other | 250 |
| 310___ | 7.350652e-05 | 3.040135e-05 | -4.310517e-05 | other | 123 |
| 311___ | 3.559568e-05 | 5.896858e-06 | -2.969882e-05 | other | 254 |
| 312___ | 3.054494e-05 | 2.224148e-05 | -8.303462e-06 | other | 296 |
| 313___ | 2.907171e-05 | 7.115461e-06 | -2.195625e-05 | other | 311 |
| 314___ | 2.288937e-05 | 3.143992e-05 | 8.550544e-06 | other | 395 |
| 315___ | 1.558845e-05 | 1.982681e-06 | -1.360577e-05 | other | 580 |
| 316___ | 1.448927e-05 | 6.333613e-06 | -8.155654e-06 | other | 624 |
| 317___ | 1.134417e-05 | 7.667768e-06 | -3.676400e-06 | other | 797 |
| 318___ | 1.120360e-05 | 8.344771e-06 | -2.858826e-06 | other | 807 |
| 319___ | 1.113461e-05 | 7.691499e-07 | -1.036546e-05 | other | 812 |
| 32___ | 2.659207e-04 | 3.391338e-04 | 7.321315e-05 | other | 34 |
| 320___ | 9.320930e-06 | 1.738357e-06 | -7.582573e-06 | other | 970 |
| 321___ | 4.758580e-04 | 4.871288e-04 | 1.127079e-05 | other | 19 |
| 322___ | 4.109683e-04 | 2.386360e-04 | -1.723323e-04 | other | 22 |
| 323___ | 2.443595e-04 | 3.106113e-05 | -2.132984e-04 | other | 37 |
| 324___ | 1.291615e-04 | 1.215317e-04 | -7.629775e-06 | other | 70 |
| 325___ | 2.731511e-05 | 1.991416e-05 | -7.400957e-06 | other | 331 |
| 326___ | 2.723284e-05 | 3.042749e-05 | 3.194652e-06 | other | 332 |
| 327___ | 2.605563e-05 | 7.121782e-06 | -1.893384e-05 | other | 347 |
| 328___ | 2.561275e-05 | 9.605731e-06 | -1.600702e-05 | other | 353 |
| 329___ | 8.449815e-05 | 2.003453e-05 | -6.446362e-05 | other | 107 |
| 33___ | 8.371576e-05 | 1.184969e-04 | 3.478110e-05 | other | 108 |
| 330___ | 8.219366e-05 | 4.823878e-05 | -3.395488e-05 | other | 110 |
| 331___ | 7.794226e-05 | 7.278825e-06 | -7.066344e-05 | other | 116 |
| 332___ | 6.322589e-05 | 5.296122e-06 | -5.792977e-05 | other | 143 |
| 333___ | 1.808260e-04 | 2.032569e-05 | -1.605004e-04 | other | 50 |
| 334___ | 4.589494e-05 | 3.630240e-09 | -4.589131e-05 | other | 197 |
| 335___ | 3.013767e-04 | 4.297014e-05 | -2.584066e-04 | other | 30 |
| 336___ | 2.825407e-04 | 2.263536e-05 | -2.599053e-04 | other | 32 |
| 337___ | 5.581051e-05 | 1.341677e-05 | -4.239374e-05 | other | 162 |
| 338___ | 5.137104e-05 | 4.136785e-06 | -4.723425e-05 | other | 176 |
| 339___ | 3.299745e-05 | 8.427732e-06 | -2.456972e-05 | other | 274 |
| 34___ | 6.412271e-05 | 8.952333e-05 | 2.540062e-05 | other | 141 |
| 340___ | 2.667051e-05 | 1.318683e-05 | -1.348368e-05 | other | 339 |
| 341___ | 2.477069e-05 | 6.546084e-06 | -1.822461e-05 | other | 365 |
| 342___ | 1.868038e-05 | 3.678263e-06 | -1.500211e-05 | other | 484 |
| 343___ | 1.801056e-05 | 3.051310e-06 | -1.495925e-05 | other | 502 |
| 344___ | 1.786819e-05 | 8.811448e-06 | -9.056739e-06 | other | 506 |
| 345___ | 7.233042e-06 | 3.328682e-06 | -3.904360e-06 | other | 1250 |
| 346___ | 7.102358e-06 | 5.151363e-06 | -1.950995e-06 | other | 1273 |
| 347___ | 7.096784e-06 | 1.117101e-06 | -5.979683e-06 | other | 1274 |
| 348___ | 6.023519e-06 | 9.637247e-07 | -5.059794e-06 | other | 1501 |
| 349___ | 5.851976e-06 | 2.989433e-06 | -2.862542e-06 | other | 1545 |
| 35___ | 2.456876e-05 | 4.002870e-05 | 1.545994e-05 | other | 368 |
| 350___ | 5.755126e-06 | 1.657232e-06 | -4.097893e-06 | other | 1571 |
| 351___ | 5.693515e-06 | 2.432663e-06 | -3.260852e-06 | other | 1588 |
| 352___ | 1.369894e-04 | 3.417201e-04 | 2.047307e-04 | other | 66 |
| 353___ | 4.758580e-05 | 1.997795e-04 | 1.521937e-04 | other | 190 |
| 354___ | 3.477424e-04 | 8.157094e-05 | -2.661715e-04 | other | 26 |
| 355___ | 2.583229e-04 | 2.894556e-04 | 3.113269e-05 | other | 35 |
| 356___ | 4.018357e-05 | 1.878571e-05 | -2.139786e-05 | other | 225 |
| 357___ | 3.477424e-05 | 8.403271e-06 | -2.637097e-05 | other | 260 |
| 358___ | 2.964361e-05 | 1.034821e-04 | 7.383850e-05 | other | 305 |
| 359___ | 1.144469e-04 | 9.052624e-09 | -1.144378e-04 | other | 79 |
| 36___ | 1.808260e-03 | 8.962423e-04 | -9.120182e-04 | neo-endemic | 5 |
| 360___ | 3.023847e-05 | 2.099901e-05 | -9.239463e-06 | other | 299 |
| 361___ | 2.456876e-05 | 8.875664e-06 | -1.569309e-05 | other | 368 |
| 362___ | 2.443595e-05 | 1.245297e-05 | -1.198298e-05 | other | 370 |
| 363___ | 2.312354e-05 | 2.369180e-05 | 5.682603e-07 | other | 391 |
| 364___ | 2.277406e-05 | 7.003495e-06 | -1.577057e-05 | other | 397 |
| 365___ | 1.435127e-04 | 7.938922e-05 | -6.412351e-05 | other | 63 |
| 366___ | 1.189645e-04 | 2.148619e-05 | -9.747832e-05 | other | 76 |
| 367___ | 2.054841e-04 | 1.625358e-08 | -2.054679e-04 | other | 44 |
| 368___ | 7.534419e-05 | 4.042876e-05 | -3.491543e-05 | other | 120 |
| 369___ | 6.797972e-05 | 6.320342e-06 | -6.165937e-05 | other | 133 |
| 37___ | 1.130163e-03 | 1.957058e-03 | 8.268952e-04 | meso-endemic | 8 |
| 370___ | 4.520651e-04 | 3.575787e-08 | -4.520294e-04 | other | 20 |
| 371___ | 9.041302e-04 | 2.486922e-04 | -6.554380e-04 | other | 10 |
| 372___ | 1.965500e-04 | 4.626448e-05 | -1.502856e-04 | other | 46 |
| 373___ | 1.369894e-04 | 5.996121e-05 | -7.702822e-05 | other | 66 |
| 374___ | 1.004589e-04 | 6.885180e-06 | -9.357373e-05 | other | 90 |
| 375___ | 1.102598e-04 | 1.954259e-05 | -9.071720e-05 | other | 82 |
| 376___ | 1.089314e-04 | 3.756057e-05 | -7.137078e-05 | other | 83 |
| 377___ | 9.418023e-05 | 2.944702e-05 | -6.473321e-05 | other | 96 |
| 378___ | 5.870976e-05 | 7.586922e-05 | 1.715946e-05 | other | 154 |
| 379___ | 3.675326e-05 | 2.170961e-05 | -1.504365e-05 | other | 246 |
| 38___ | 1.956992e-05 | 1.085814e-05 | -8.711777e-06 | other | 462 |
| 380___ | 3.161295e-05 | 1.208417e-05 | -1.952877e-05 | other | 286 |
| 381___ | 3.054494e-05 | 8.321486e-06 | -2.222345e-05 | other | 296 |
| 382___ | 2.925988e-05 | 1.085457e-05 | -1.840531e-05 | other | 309 |
| 383___ | 2.226922e-05 | 8.843135e-06 | -1.342608e-05 | other | 406 |
| 384___ | 1.801056e-05 | 1.732309e-06 | -1.627825e-05 | other | 502 |
| 385___ | 7.534419e-05 | 4.831432e-05 | -2.702987e-05 | other | 120 |
| 386___ | 5.166458e-05 | 3.578376e-05 | -1.588083e-05 | other | 175 |
| 387___ | 3.183557e-05 | 7.953268e-06 | -2.388230e-05 | other | 284 |
| 388___ | 2.674942e-05 | 4.289230e-05 | 1.614289e-05 | other | 338 |
| 389___ | 5.650814e-04 | 2.012442e-04 | -3.638372e-04 | other | 16 |
| 39___ | 7.794226e-05 | 1.943760e-05 | -5.850466e-05 | other | 116 |
| 390___ | 4.305382e-05 | 5.845142e-05 | 1.539760e-05 | other | 210 |
| 391___ | 4.244743e-05 | 2.840161e-05 | -1.404582e-05 | other | 213 |
| 392___ | 4.224908e-05 | 4.088467e-06 | -3.816061e-05 | other | 214 |
| 393___ | 4.072659e-05 | 4.482355e-06 | -3.624423e-05 | other | 222 |
| 394___ | 2.723284e-05 | 3.486406e-06 | -2.374643e-05 | other | 332 |
| 395___ | 2.031753e-05 | 2.016143e-06 | -1.830139e-05 | other | 445 |
| 396___ | 1.504376e-05 | 1.994775e-06 | -1.304899e-05 | other | 601 |
| 397___ | 1.221798e-05 | 3.855233e-06 | -8.362743e-06 | other | 740 |
| 398___ | 9.447547e-06 | 2.013047e-06 | -7.434500e-06 | other | 957 |
| 399___ | 8.744006e-06 | 4.227835e-06 | -4.516171e-06 | other | 1034 |
| 4___ | 8.145317e-05 | 7.823254e-05 | -3.220634e-06 | other | 111 |
| 40___ | 1.159141e-04 | 9.524741e-05 | -2.066672e-05 | other | 78 |
| 400___ | 8.529530e-06 | 9.422414e-07 | -7.587289e-06 | other | 1060 |
| 401___ | 1.291615e-03 | 1.535655e-04 | -1.138049e-03 | neo-endemic | 7 |
| 402___ | 6.027535e-04 | 6.599641e-04 | 5.721066e-05 | other | 15 |
| 403___ | 1.130163e-04 | 2.535538e-05 | -8.766090e-05 | other | 80 |
| 404___ | 2.659207e-04 | 1.328233e-04 | -1.330974e-04 | other | 34 |
| 405___ | 1.015877e-04 | 1.894606e-05 | -8.264160e-05 | other | 89 |
| 406___ | 1.860350e-05 | 1.625219e-06 | -1.697828e-05 | other | 486 |
| 407___ | 1.797476e-05 | 1.502893e-05 | -2.945824e-06 | other | 503 |
| 408___ | 1.086695e-05 | 4.145344e-06 | -6.721606e-06 | other | 832 |
| 409___ | 4.264765e-05 | 1.735445e-05 | -2.529321e-05 | other | 212 |
| 41___ | 1.323763e-05 | 1.047082e-09 | -1.323658e-05 | other | 683 |
| 410___ | 3.490850e-05 | 3.545571e-06 | -3.136293e-05 | other | 259 |
| 411___ | 3.172387e-05 | 3.150578e-06 | -2.857329e-05 | other | 285 |
| 412___ | 3.054494e-05 | 5.381137e-06 | -2.516380e-05 | other | 296 |
| 413___ | 1.030935e-05 | 5.404304e-06 | -4.905049e-06 | other | 877 |
| 414___ | 9.700968e-06 | 5.151312e-06 | -4.549656e-06 | other | 932 |
| 415___ | 9.359526e-06 | 3.100889e-06 | -6.258637e-06 | other | 966 |
| 416___ | 8.219366e-06 | 9.510136e-07 | -7.268352e-06 | other | 1100 |
| 417___ | 4.758580e-05 | 1.809485e-05 | -2.949095e-05 | other | 190 |
| 418___ | 4.498160e-05 | 7.975766e-06 | -3.700584e-05 | other | 201 |
| 419___ | 4.305382e-05 | 4.236547e-05 | -6.883487e-07 | other | 210 |
| 42___ | 1.808260e-04 | 1.509412e-05 | -1.657319e-04 | other | 50 |
| 420___ | 3.139341e-05 | 4.115266e-06 | -2.727814e-05 | other | 288 |
| 421___ | 2.360653e-05 | 1.374304e-05 | -9.863493e-06 | other | 383 |
| 422___ | 5.833098e-05 | 6.995193e-06 | -5.133579e-05 | other | 155 |
| 423___ | 3.948167e-05 | 9.585672e-06 | -2.989600e-05 | other | 229 |
| 424___ | 1.699493e-05 | 3.944849e-06 | -1.305008e-05 | other | 532 |
| 425___ | 1.403929e-05 | 4.802965e-06 | -9.236324e-06 | other | 644 |
| 426___ | 1.004589e-03 | 1.669130e-03 | 6.645407e-04 | meso-endemic | 9 |
| 427___ | 6.954848e-04 | 1.737355e-05 | -6.781112e-04 | other | 13 |
| 428___ | 7.410904e-05 | 1.886828e-04 | 1.145737e-04 | other | 122 |
| 429___ | 3.117690e-04 | 2.182921e-04 | -9.347694e-05 | other | 29 |
| 43___ | 1.280638e-05 | 1.054689e-06 | -1.175169e-05 | other | 706 |
| 430___ | 1.586193e-04 | 1.105759e-04 | -4.804341e-05 | other | 57 |
| 431___ | 5.318413e-04 | 1.050486e-04 | -4.267927e-04 | other | 17 |
| 432___ | 1.102598e-04 | 3.772722e-05 | -7.253257e-05 | other | 82 |
| 433___ | 6.849471e-05 | 3.473418e-05 | -3.376054e-05 | other | 132 |
| 434___ | 4.072659e-05 | 4.604166e-06 | -3.612242e-05 | other | 222 |
| 435___ | 2.059522e-05 | 1.004895e-05 | -1.054628e-05 | other | 439 |
| 436___ | 8.978453e-06 | 4.442727e-06 | -4.535726e-06 | other | 1007 |
| 437___ | 8.545654e-06 | 9.886114e-07 | -7.557043e-06 | other | 1058 |
| 438___ | 5.587949e-06 | 4.420008e-10 | -5.587507e-06 | other | 1618 |
| 439___ | 5.587949e-06 | 4.381861e-07 | -5.149763e-06 | other | 1618 |
| 44___ | 1.072515e-05 | 3.076302e-06 | -7.648849e-06 | other | 843 |
| 440___ | 5.512989e-06 | 3.195335e-06 | -2.317654e-06 | other | 1640 |
| 441___ | 6.551668e-05 | 6.641427e-05 | 8.975846e-07 | other | 138 |
| 442___ | 7.008761e-05 | 5.543855e-09 | -7.008207e-05 | other | 129 |
| 443___ | 9.320930e-05 | 7.372756e-09 | -9.320193e-05 | other | 97 |
| 444___ | 8.777963e-05 | 3.802415e-05 | -4.975549e-05 | other | 103 |
| 445___ | 4.410391e-05 | 1.069986e-05 | -3.340405e-05 | other | 205 |
| 446___ | 3.675326e-05 | 4.103422e-05 | 4.280956e-06 | other | 246 |
| 447___ | 2.756495e-05 | 8.186036e-06 | -1.937891e-05 | other | 328 |
| 448___ | 5.196151e-05 | 4.063185e-05 | -1.132966e-05 | other | 174 |
| 449___ | 4.072659e-05 | 8.142923e-06 | -3.258366e-05 | other | 222 |
| 45___ | 1.015877e-05 | 4.644428e-06 | -5.514339e-06 | other | 890 |
| 450___ | 2.916549e-05 | 8.171119e-06 | -2.099437e-05 | other | 310 |
| 451___ | 2.674942e-05 | 6.829576e-06 | -1.991984e-05 | other | 338 |
| 452___ | 9.618407e-05 | 3.831646e-05 | -5.786760e-05 | other | 94 |
| 453___ | 6.235381e-05 | 1.962881e-05 | -4.272500e-05 | other | 145 |
| 454___ | 5.909348e-05 | 1.595873e-05 | -4.313475e-05 | other | 153 |
| 455___ | 2.102628e-04 | 2.624281e-04 | 5.216525e-05 | other | 43 |
| 456___ | 6.648016e-05 | 1.862801e-05 | -4.785215e-05 | other | 136 |
| 457___ | 3.531759e-05 | 6.913559e-06 | -2.840403e-05 | other | 256 |
| 458___ | 2.964361e-05 | 1.474459e-06 | -2.816915e-05 | other | 305 |
| 459___ | 2.879396e-05 | 4.898673e-06 | -2.389528e-05 | other | 314 |
| 46___ | 9.859654e-06 | 8.616349e-07 | -8.998019e-06 | other | 917 |
| 460___ | 1.725439e-05 | 6.624631e-06 | -1.062976e-05 | other | 524 |
| 461___ | 1.225109e-05 | 2.715107e-06 | -9.535980e-06 | other | 738 |
| 462___ | 3.602112e-05 | 2.849232e-09 | -3.601828e-05 | other | 251 |
| 463___ | 2.398223e-05 | 1.110051e-05 | -1.288172e-05 | other | 377 |
| 464___ | 2.348390e-05 | 1.811581e-05 | -5.368096e-06 | other | 385 |
| 465___ | 2.330233e-05 | 1.843189e-09 | -2.330048e-05 | other | 388 |
| 466___ | 2.064224e-05 | 1.996282e-06 | -1.864596e-05 | other | 438 |
| 467___ | 4.367779e-05 | 3.392275e-05 | -9.755037e-06 | other | 207 |
| 468___ | 3.798867e-05 | 2.833669e-06 | -3.515500e-05 | other | 238 |
| 469___ | 2.483874e-05 | 1.720410e-05 | -7.634641e-06 | other | 364 |
| 47___ | 7.591354e-06 | 1.079876e-05 | 3.207411e-06 | other | 1191 |
| 470___ | 2.511473e-04 | 1.074039e-04 | -1.437433e-04 | other | 36 |
| 471___ | 6.648016e-05 | 1.763197e-05 | -4.884819e-05 | other | 136 |
| 472___ | 1.051314e-04 | 5.013550e-05 | -5.499592e-05 | other | 86 |
| 473___ | 5.196151e-05 | 7.730290e-06 | -4.423122e-05 | other | 174 |
| 474___ | 9.320930e-05 | 3.531953e-05 | -5.788977e-05 | other | 97 |
| 475___ | 4.709012e-05 | 4.027978e-06 | -4.306214e-05 | other | 192 |
| 476___ | 3.782972e-05 | 3.329100e-06 | -3.450062e-05 | other | 239 |
| 477___ | 2.477069e-05 | 2.140332e-06 | -2.263036e-05 | other | 365 |
| 478___ | 2.142489e-05 | 5.868434e-06 | -1.555645e-05 | other | 422 |
| 479___ | 2.117401e-05 | 1.674841e-09 | -2.117234e-05 | other | 427 |
| 48___ | 7.591354e-06 | 2.628153e-05 | 1.869017e-05 | other | 1191 |
| 480___ | 2.000288e-05 | 1.869942e-06 | -1.813294e-05 | other | 452 |
| 481___ | 1.165116e-05 | 1.020156e-06 | -1.063101e-05 | other | 776 |
| 482___ | 9.711388e-06 | 2.056928e-06 | -7.654460e-06 | other | 931 |
| 483___ | 9.507153e-06 | 3.151960e-06 | -6.355193e-06 | other | 951 |
| 484___ | 9.014259e-06 | 4.188186e-06 | -4.826074e-06 | other | 1003 |
| 485___ | 8.987378e-06 | 2.567102e-06 | -6.420276e-06 | other | 1006 |
| 486___ | 7.681650e-06 | 3.433618e-06 | -4.248032e-06 | other | 1177 |
| 487___ | 7.572280e-06 | 2.453788e-06 | -5.118492e-06 | other | 1194 |
| 488___ | 4.002347e-06 | 9.110017e-07 | -3.091346e-06 | other | 2259 |
| 489___ | 3.995273e-06 | 2.514326e-06 | -1.480946e-06 | other | 2263 |
| 49___ | 7.584985e-06 | 2.600874e-06 | -4.984111e-06 | other | 1192 |
| 490___ | 3.782972e-06 | 3.917105e-07 | -3.391261e-06 | other | 2390 |
| 491___ | 3.778229e-06 | 1.239022e-06 | -2.539207e-06 | other | 2393 |
| 492___ | 3.778229e-06 | 3.588184e-07 | -3.419411e-06 | other | 2393 |
| 493___ | 3.771924e-06 | 4.304886e-07 | -3.341436e-06 | other | 2397 |
| 494___ | 3.771924e-06 | 8.199410e-07 | -2.951983e-06 | other | 2397 |
| 495___ | 3.708491e-06 | 1.066736e-06 | -2.641756e-06 | other | 2438 |
| 496___ | 3.679814e-06 | 1.226428e-06 | -2.453386e-06 | other | 2457 |
| 497___ | 3.661929e-06 | 6.966459e-07 | -2.965283e-06 | other | 2469 |
| 498___ | 3.130645e-06 | 3.712458e-06 | 5.818128e-07 | other | 2888 |
| 499___ | 3.110183e-06 | 1.279921e-06 | -1.830262e-06 | other | 2907 |
| 5___ | 2.511473e-04 | 3.377366e-04 | 8.658934e-05 | other | 36 |
| 50___ | 6.987096e-06 | 4.411483e-06 | -2.575613e-06 | other | 1294 |
| 500___ | 3.088931e-06 | 7.886079e-07 | -2.300324e-06 | other | 2927 |
| 501___ | 3.058627e-06 | 2.419341e-10 | -3.058385e-06 | other | 2956 |
| 502___ | 3.057593e-06 | 4.329675e-06 | 1.272082e-06 | other | 2957 |
| 503___ | 3.043185e-06 | 2.074385e-06 | -9.688000e-07 | other | 2971 |
| 504___ | 3.012763e-06 | 1.435897e-06 | -1.576866e-06 | other | 3001 |
| 505___ | 2.998774e-06 | 1.811016e-06 | -1.187758e-06 | other | 3015 |
| 506___ | 2.997779e-06 | 6.074031e-07 | -2.390376e-06 | other | 3016 |
| 507___ | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | other | NA |
| 51___ | 2.583229e-04 | 4.403145e-04 | 1.819916e-04 | other | 35 |
| 52___ | 2.054841e-04 | 5.069268e-04 | 3.014426e-04 | other | 44 |
| 53___ | 7.534419e-05 | 3.601855e-05 | -3.932564e-05 | other | 120 |
| 54___ | 1.310334e-04 | 2.537250e-05 | -1.056609e-04 | other | 69 |
| 55___ | 6.648016e-05 | 1.839042e-05 | -4.808974e-05 | other | 136 |
| 56___ | 3.948167e-05 | 1.413389e-05 | -2.534778e-05 | other | 229 |
| 57___ | 3.948167e-05 | 5.386139e-05 | 1.437972e-05 | other | 229 |
| 58___ | 3.913984e-05 | 5.783681e-05 | 1.869697e-05 | other | 231 |
| 59___ | 3.287746e-05 | 2.911583e-05 | -3.761631e-06 | other | 275 |
| 6___ | 5.795707e-05 | 1.104020e-04 | 5.244493e-05 | other | 156 |
| 60___ | 1.883605e-04 | 2.228697e-04 | 3.450926e-05 | other | 48 |
| 61___ | 1.586193e-04 | 9.409158e-04 | 7.822965e-04 | other | 57 |
| 62___ | 3.931001e-04 | 2.258312e-04 | -1.672689e-04 | other | 23 |
| 63___ | 3.013767e-04 | 4.172849e-05 | -2.596482e-04 | other | 30 |
| 64___ | 2.825407e-04 | 1.212211e-04 | -1.613196e-04 | other | 32 |
| 65___ | 1.845164e-04 | 8.647190e-05 | -9.804447e-05 | other | 49 |
| 66___ | 1.772804e-04 | 4.721267e-04 | 2.948462e-04 | other | 51 |
| 67___ | 1.532424e-04 | 2.035616e-04 | 5.031916e-05 | other | 59 |
| 68___ | 1.506884e-03 | 4.977381e-04 | -1.009146e-03 | neo-endemic | 6 |
| 69___ | 2.318283e-04 | 1.788137e-04 | -5.301458e-05 | other | 39 |
| 7___ | 5.196151e-05 | 4.778721e-05 | -4.174294e-06 | other | 174 |
| 70___ | 1.051314e-04 | 4.720912e-05 | -5.792230e-05 | other | 86 |
| 71___ | 1.051314e-04 | 4.660956e-04 | 3.609642e-04 | other | 86 |
| 72___ | 5.022946e-04 | 1.462622e-03 | 9.603271e-04 | other | 18 |
| 73___ | 3.348630e-04 | 5.881803e-04 | 2.533172e-04 | other | 27 |
| 74___ | 6.458073e-04 | 5.108267e-08 | -6.457562e-04 | other | 14 |
| 75___ | 3.948167e-05 | 1.461606e-04 | 1.066789e-04 | other | 229 |
| 76___ | 3.751578e-05 | 8.002141e-05 | 4.250564e-05 | other | 241 |
| 77___ | 3.690327e-05 | 9.526445e-06 | -2.737683e-05 | other | 245 |
| 78___ | 3.675326e-05 | 3.485838e-05 | -1.894879e-06 | other | 246 |
| 79___ | 3.573637e-05 | 2.826709e-09 | -3.573355e-05 | other | 253 |
| 8___ | 4.566314e-05 | 1.687415e-05 | -2.878899e-05 | other | 198 |
| 80___ | 3.299745e-05 | 3.187543e-05 | -1.122019e-06 | other | 274 |
| 81___ | 3.275834e-05 | 2.286138e-05 | -9.896959e-06 | other | 276 |
| 82___ | 3.217545e-05 | 2.611769e-05 | -6.057764e-06 | other | 281 |
| 83___ | 3.183557e-05 | 1.021483e-05 | -2.162074e-05 | other | 284 |
| 84___ | 1.709131e-05 | 7.851706e-06 | -9.239602e-06 | other | 529 |
| 85___ | 5.693515e-06 | 7.575179e-06 | 1.881664e-06 | other | 1588 |
| 86___ | 8.219366e-04 | 9.147983e-04 | 9.286170e-05 | other | 11 |
| 87___ | 2.216005e-05 | 1.436167e-05 | -7.798385e-06 | other | 408 |
| 88___ | 2.142489e-05 | 2.754671e-05 | 6.121826e-06 | other | 422 |
| 89___ | 2.107530e-05 | 4.557120e-05 | 2.449591e-05 | other | 429 |
| 9___ | 4.305382e-04 | 4.710688e-04 | 4.053056e-05 | other | 21 |
| 90___ | 6.367114e-05 | 1.603466e-05 | -4.763649e-05 | other | 142 |
| 91___ | 6.367114e-05 | 3.151921e-04 | 2.515210e-04 | other | 142 |
| 92___ | 5.795707e-05 | 1.907411e-04 | 1.327841e-04 | other | 156 |
| 93___ | 8.072591e-05 | 8.565735e-06 | -7.216018e-05 | other | 112 |
| 94___ | 7.350652e-05 | 8.790881e-05 | 1.440229e-05 | other | 123 |
| 95___ | 3.240610e-05 | 5.726527e-05 | 2.485917e-05 | other | 279 |
| 96___ | 2.324242e-05 | 2.470511e-05 | 1.462687e-06 | other | 389 |
| 97___ | 1.186523e-05 | 1.136541e-05 | -4.998158e-07 | other | 762 |
| 98___ | 8.219366e-04 | 4.798358e-03 | 3.976421e-03 | other | 11 |
| 99___ | 4.388982e-05 | 3.253124e-06 | -4.063669e-05 | other | 206 |
| abbreviata | 1.506884e-03 | 1.436124e-03 | -7.075948e-05 | meso-endemic | 6 |
| acanthaster | 9.041302e-04 | 2.500786e-03 | 1.596655e-03 | other | 10 |
| acanthoclada | 9.132629e-05 | 1.844050e-04 | 9.307874e-05 | other | 99 |
| acinacea | 7.233042e-05 | 7.218576e-05 | -1.446581e-07 | other | 125 |
| aciphylla | 7.534419e-04 | 3.224805e-04 | -4.309613e-04 | other | 12 |
| acoma | 6.458073e-04 | 3.464592e-04 | -2.993482e-04 | other | 14 |
| acradenia | 5.137104e-05 | 5.621538e-05 | 4.844342e-06 | other | 176 |
| acrionastes | 1.808260e-03 | 7.818173e-04 | -1.026443e-03 | neo-endemic | 5 |
| acuaria | 2.825407e-04 | 2.234867e-08 | -2.825183e-04 | other | 32 |
| aculeatissima | 2.659207e-04 | 7.569400e-04 | 4.910193e-04 | other | 34 |
| acuminata | 6.551668e-05 | 1.885246e-05 | -4.666422e-05 | other | 138 |
| acutata | 3.616521e-04 | 4.275355e-04 | 6.588346e-05 | other | 25 |
| adinophylla | 9.041302e-03 | 1.786382e-02 | 8.822520e-03 | paleo-endemic | 1 |
| adnata | 1.506884e-03 | 4.796539e-04 | -1.027230e-03 | neo-endemic | 6 |
| adoxa | 7.862002e-05 | 1.820600e-05 | -6.041402e-05 | other | 115 |
| adsurgens | 5.833098e-05 | 1.054178e-05 | -4.778920e-05 | other | 155 |
| adunca | 5.650814e-04 | 7.893862e-04 | 2.243048e-04 | other | 16 |
| aemula | 5.022946e-04 | 3.673275e-04 | -1.349671e-04 | other | 18 |
| aestivalis | 3.767209e-04 | 2.933933e-04 | -8.332766e-05 | other | 24 |
| alata | 1.808260e-04 | 4.009575e-04 | 2.201315e-04 | other | 50 |
| alcockii | 1.291615e-03 | 1.606220e-03 | 3.146051e-04 | meso-endemic | 7 |
| alexandri | 2.260326e-03 | 9.152274e-03 | 6.891948e-03 | paleo-endemic | 4 |
| alpina | 5.318413e-04 | 5.692479e-04 | 3.740658e-05 | other | 17 |
| amblygona | 1.039230e-04 | 2.060459e-04 | 1.021229e-04 | other | 87 |
| amblyophylla | 4.520651e-03 | 1.408732e-02 | 9.566664e-03 | paleo-endemic | 2 |
| ammobia | 8.219366e-04 | 7.910600e-04 | -3.087660e-05 | other | 11 |
| ampliceps | 1.116210e-04 | 5.256877e-05 | -5.905225e-05 | other | 81 |
| amyctica | 3.013767e-03 | 1.040566e-03 | -1.973201e-03 | neo-endemic | 3 |
| anasilla | 1.506884e-03 | 3.428075e-03 | 1.921192e-03 | meso-endemic | 6 |
| anastema | 1.506884e-03 | 2.293590e-03 | 7.867058e-04 | meso-endemic | 6 |
| anaticeps | 6.954848e-04 | 2.142965e-03 | 1.447480e-03 | other | 13 |
| anceps | 2.511473e-04 | 2.015837e-04 | -4.956357e-05 | other | 36 |
| ancistrocarpa | 4.346780e-05 | 7.697054e-05 | 3.350274e-05 | other | 208 |
| ancistrophylla | 1.238535e-04 | 2.571802e-05 | -9.813544e-05 | other | 73 |
| andrewsii | 2.205196e-04 | 4.744428e-04 | 2.539233e-04 | other | 41 |
| aneura | 1.094589e-05 | 4.233587e-06 | -6.712300e-06 | other | 826 |
| anfractuosa | 5.650814e-04 | 5.545739e-04 | -1.050747e-05 | other | 16 |
| angusta | 5.650814e-04 | 1.156121e-03 | 5.910400e-04 | other | 16 |
| anthochaera | 4.758580e-04 | 1.535120e-04 | -3.223460e-04 | other | 19 |
| aphanoclada | 2.260326e-03 | 6.950566e-03 | 4.690240e-03 | paleo-endemic | 4 |
| aphylla | 3.013767e-03 | 1.205790e-02 | 9.044135e-03 | paleo-endemic | 3 |
| applanata | 2.825407e-04 | 2.195575e-03 | 1.913034e-03 | other | 32 |
| aprepta | 2.825407e-04 | 2.234867e-08 | -2.825183e-04 | other | 32 |
| aprica | 9.041302e-03 | 2.293302e-03 | -6.748001e-03 | neo-endemic | 1 |
| araneosa | 3.013767e-03 | 9.974512e-04 | -2.016316e-03 | neo-endemic | 3 |
| arcuatilis | 1.506884e-03 | 1.846046e-04 | -1.322279e-03 | neo-endemic | 6 |
| areolata | 1.291615e-03 | 1.021653e-07 | -1.291512e-03 | neo-endemic | 7 |
| argutifolia | 3.013767e-03 | 5.086852e-03 | 2.073085e-03 | meso-endemic | 3 |
| argyraea | 1.705906e-04 | 3.020693e-04 | 1.314787e-04 | other | 53 |
| argyrodendron | 3.767209e-04 | 5.441393e-05 | -3.223070e-04 | other | 24 |
| argyrophylla | 2.825407e-04 | 2.648535e-05 | -2.560553e-04 | other | 32 |
| arida | 2.583229e-04 | 7.907022e-05 | -1.792527e-04 | other | 35 |
| armitii | 6.027535e-04 | 9.113025e-05 | -5.116232e-04 | other | 15 |
| arrecta | 6.458073e-04 | 3.388787e-04 | -3.069286e-04 | other | 14 |
| ashbyae | 9.041302e-04 | 3.632112e-03 | 2.727981e-03 | other | 10 |
| aspera | 3.013767e-04 | 8.807847e-05 | -2.132983e-04 | other | 30 |
| asperulacea | 1.965500e-04 | 1.088829e-04 | -8.766714e-05 | other | 46 |
| assimilis | 1.051314e-04 | 3.533684e-05 | -6.979458e-05 | other | 86 |
| ataxiphylla | 1.004589e-03 | 3.729739e-03 | 2.725149e-03 | meso-endemic | 9 |
| atkinsiana | 4.758580e-04 | 6.310771e-04 | 1.552191e-04 | other | 19 |
| atrox | 9.041302e-03 | 5.763760e-03 | -3.277542e-03 | neo-endemic | 1 |
| attenuata | 8.219366e-04 | 8.138164e-04 | -8.120184e-06 | other | 11 |
| aulacocarpa | 1.051314e-04 | 5.081359e-05 | -5.431783e-05 | other | 86 |
| aulacophylla | 5.022946e-04 | 2.950568e-04 | -2.072377e-04 | other | 18 |
| auratiflora | 3.013767e-03 | 1.277168e-02 | 9.757913e-03 | paleo-endemic | 3 |
| aureocrinita | 1.004589e-03 | 3.679445e-04 | -6.366446e-04 | neo-endemic | 9 |
| auricoma | 1.506884e-03 | 2.956324e-03 | 1.449440e-03 | meso-endemic | 6 |
| auriculiformis | 8.371576e-05 | 2.505897e-04 | 1.668740e-04 | other | 108 |
| auronitens | 7.534419e-04 | 1.084753e-03 | 3.313107e-04 | other | 12 |
| ausfeldii | 6.027535e-04 | 4.767715e-08 | -6.027058e-04 | other | 15 |
| axillaris | 8.219366e-04 | 2.547651e-03 | 1.725715e-03 | other | 11 |
| ayersiana | 7.597733e-05 | 5.308986e-05 | -2.288747e-05 | other | 119 |
| baeuerlenii | 6.027535e-04 | 6.645766e-04 | 6.182310e-05 | other | 15 |
| baileyana | 7.534419e-04 | 1.348035e-04 | -6.186383e-04 | other | 12 |
| bakeri | 6.027535e-04 | 2.710215e-03 | 2.107461e-03 | other | 15 |
| balsamea | 4.758580e-04 | 1.138892e-04 | -3.619688e-04 | other | 19 |
| bancroftiorum | 1.144469e-04 | 9.052624e-09 | -1.144378e-04 | other | 79 |
| barattensis | 3.013767e-03 | 7.124868e-03 | 4.111101e-03 | paleo-endemic | 3 |
| barringtonensis | 1.291615e-03 | 1.517307e-03 | 2.256928e-04 | meso-endemic | 7 |
| bartleana | 4.520651e-03 | 3.575787e-07 | -4.520294e-03 | neo-endemic | 2 |
| basedowii | 3.229037e-04 | 6.167092e-04 | 2.938056e-04 | other | 28 |
| baueri | 3.616521e-04 | 9.258915e-04 | 5.642394e-04 | other | 25 |
| baxteri | 1.004589e-03 | 3.822126e-03 | 2.817537e-03 | meso-endemic | 9 |
| beadleana | 4.520651e-03 | 1.375688e-02 | 9.236227e-03 | paleo-endemic | 2 |
| beauverdiana | 3.477424e-04 | 4.052933e-05 | -3.072131e-04 | other | 26 |
| beckleri | 1.674315e-04 | 1.375453e-04 | -2.988619e-05 | other | 54 |
| benthamii | 1.130163e-03 | 3.384190e-04 | -7.917438e-04 | neo-endemic | 8 |
| betchei | 7.534419e-04 | 2.232045e-04 | -5.302373e-04 | other | 12 |
| bidentata | 1.674315e-04 | 4.496156e-04 | 2.821841e-04 | other | 54 |
| bifaria | 1.506884e-03 | 7.277486e-04 | -7.791351e-04 | neo-endemic | 6 |
| biflora | 6.954848e-04 | 1.195532e-03 | 5.000474e-04 | other | 13 |
| binata | 7.534419e-04 | 4.722852e-04 | -2.811566e-04 | other | 12 |
| binervata | 2.318283e-04 | 2.074490e-05 | -2.110834e-04 | other | 39 |
| binervia | 3.348630e-04 | 2.209943e-04 | -1.138688e-04 | other | 27 |
| bivenosa | 4.589494e-05 | 7.553815e-05 | 2.964321e-05 | other | 197 |
| blakei | 1.027421e-04 | 6.605827e-04 | 5.578406e-04 | other | 88 |
| blakelyi | 3.013767e-04 | 4.671436e-04 | 1.657668e-04 | other | 30 |
| blayana | 3.013767e-03 | 5.415040e-04 | -2.472263e-03 | neo-endemic | 3 |
| boormanii | 4.520651e-04 | 5.757662e-04 | 1.237011e-04 | other | 20 |
| brachybotrya | 7.063517e-05 | 1.471852e-05 | -5.591666e-05 | other | 128 |
| brachyclada | 3.616521e-04 | 1.175988e-03 | 8.143363e-04 | other | 25 |
| brachyphylla | 5.022946e-04 | 8.905851e-04 | 3.882905e-04 | other | 18 |
| brachypoda | 1.808260e-03 | 2.244015e-03 | 4.357546e-04 | meso-endemic | 5 |
| brachystachya | 3.798867e-05 | 2.516907e-05 | -1.281959e-05 | other | 238 |
| bracteolata | 1.291615e-03 | 1.436652e-03 | 1.450379e-04 | meso-endemic | 7 |
| brassii | 5.318413e-04 | 1.158921e-03 | 6.270801e-04 | other | 17 |
| brockii | 1.004589e-03 | 2.920476e-03 | 1.915887e-03 | meso-endemic | 9 |
| bromilowiana | 1.004589e-03 | 1.770965e-03 | 7.663762e-04 | meso-endemic | 9 |
| browniana | 2.318283e-04 | 4.626265e-04 | 2.307982e-04 | other | 39 |
| bulgaensis | 2.260326e-03 | 6.533363e-04 | -1.606989e-03 | neo-endemic | 4 |
| burkittii | 4.346780e-05 | 2.406079e-05 | -1.940701e-05 | other | 208 |
| buxifolia | 7.233042e-05 | 1.773820e-04 | 1.050516e-04 | other | 125 |
| caesiella | 4.520651e-04 | 1.115294e-03 | 6.632290e-04 | other | 20 |
| calamifolia | 9.132629e-05 | 5.216657e-05 | -3.915971e-05 | other | 99 |
| calantha | 1.130163e-03 | 1.174237e-03 | 4.407440e-05 | meso-endemic | 8 |
| calcicola | 8.693560e-05 | 8.214313e-06 | -7.872129e-05 | other | 104 |
| camptoclada | 2.825407e-04 | 4.585115e-04 | 1.759708e-04 | other | 32 |
| campylophylla | 1.291615e-03 | 2.723046e-03 | 1.431431e-03 | meso-endemic | 7 |
| cana | 3.616521e-04 | 1.285143e-04 | -2.331378e-04 | other | 25 |
| cangaiensis | 4.520651e-03 | 4.788326e-04 | -4.041819e-03 | neo-endemic | 2 |
| cardiophylla | 5.650814e-04 | 2.138152e-04 | -3.512662e-04 | other | 16 |
| carneorum | 3.229037e-04 | 7.078652e-04 | 3.849615e-04 | other | 28 |
| carnosula | 2.260326e-03 | 5.380049e-03 | 3.119724e-03 | paleo-endemic | 4 |
| caroleae | 1.189645e-04 | 4.981365e-05 | -6.915085e-05 | other | 76 |
| catenulata | 1.532424e-04 | 8.273224e-05 | -7.051017e-05 | other | 59 |
| cavealis | 1.506884e-03 | 3.656891e-03 | 2.150007e-03 | meso-endemic | 6 |
| cedroides | 1.808260e-03 | 4.662524e-03 | 2.854264e-03 | meso-endemic | 5 |
| celastrifolia | 4.305382e-04 | 8.602593e-04 | 4.297211e-04 | other | 21 |
| celsa | 6.027535e-04 | 4.034102e-04 | -1.993433e-04 | other | 15 |
| chartacea | 5.318413e-04 | 5.367480e-04 | 4.906701e-06 | other | 17 |
| cheelii | 2.659207e-04 | 1.809641e-04 | -8.495653e-05 | other | 34 |
| chinchillensis | 1.004589e-03 | 1.711744e-03 | 7.071549e-04 | meso-endemic | 9 |
| chisholmii | 1.349448e-04 | 1.067399e-08 | -1.349341e-04 | other | 67 |
| chrysotricha | 4.520651e-03 | 5.207260e-03 | 6.866087e-04 | meso-endemic | 2 |
| citrinoviridis | 1.772804e-04 | 2.045159e-05 | -1.568289e-04 | other | 51 |
| clelandii | 2.205196e-04 | 1.818532e-04 | -3.866640e-05 | other | 41 |
| clunies | 4.520651e-03 | 1.818097e-03 | -2.702554e-03 | neo-endemic | 2 |
| clydonophora | 1.506884e-03 | 3.188094e-03 | 1.681211e-03 | meso-endemic | 6 |
| cochlearis | 1.705906e-04 | 2.671033e-04 | 9.651273e-05 | other | 53 |
| cognata | 5.650814e-04 | 1.802979e-04 | -3.847834e-04 | other | 16 |
| colei | 4.684613e-05 | 3.705478e-09 | -4.684242e-05 | other | 193 |
| colletioides | 5.833098e-05 | 1.279603e-04 | 6.962930e-05 | other | 155 |
| complanata | 1.130163e-04 | 2.001405e-04 | 8.712423e-05 | other | 80 |
| concurrens | 1.883605e-04 | 2.784710e-05 | -1.605134e-04 | other | 48 |
| conferta | 8.951784e-05 | 6.448137e-05 | -2.503648e-05 | other | 101 |
| congesta | 6.027535e-04 | 2.049068e-03 | 1.446315e-03 | other | 15 |
| consobrina | 6.954848e-04 | 1.281995e-03 | 5.865106e-04 | other | 13 |
| conspersa | 3.013767e-04 | 4.142095e-04 | 1.128328e-04 | other | 30 |
| constablei | 9.041302e-03 | 3.623151e-02 | 2.719021e-02 | paleo-endemic | 1 |
| continua | 1.329603e-04 | 1.970188e-04 | 6.405845e-05 | other | 68 |
| coolgardiensis | 1.015877e-04 | 1.628077e-04 | 6.122007e-05 | other | 89 |
| coriacea | 4.709012e-05 | 3.724778e-09 | -4.708639e-05 | other | 192 |
| costata | 8.219366e-04 | 2.172506e-03 | 1.350569e-03 | other | 11 |
| courtii | 9.041302e-03 | 6.064724e-03 | -2.976578e-03 | neo-endemic | 1 |
| covenyi | 1.506884e-03 | 3.143006e-03 | 1.636122e-03 | meso-endemic | 6 |
| cowleana | 6.150546e-05 | 1.050247e-04 | 4.351924e-05 | other | 147 |
| craspedocarpa | 2.009178e-04 | 7.912177e-05 | -1.217961e-04 | other | 45 |
| crassa | 9.418023e-05 | 2.596380e-05 | -6.821644e-05 | other | 96 |
| crassicarpa | 1.482181e-04 | 1.172389e-08 | -1.482063e-04 | other | 61 |
| crassiuscula | 6.027535e-04 | 6.187676e-04 | 1.601409e-05 | other | 15 |
| cremiflora | 6.458073e-04 | 5.288980e-04 | -1.169093e-04 | other | 14 |
| cultriformis | 2.318283e-04 | 3.304559e-04 | 9.862759e-05 | other | 39 |
| cupularis | 8.001152e-05 | 3.408263e-05 | -4.592890e-05 | other | 113 |
| curranii | 6.954848e-04 | 1.242627e-03 | 5.471421e-04 | other | 13 |
| curvata | 1.130163e-03 | 2.382844e-03 | 1.252681e-03 | meso-endemic | 8 |
| cuspidifolia | 3.117690e-04 | 2.172394e-03 | 1.860625e-03 | other | 29 |
| cuthbertsonii | 5.446568e-05 | 1.962704e-04 | 1.418048e-04 | other | 166 |
| cyclops | 1.369894e-04 | 6.521050e-04 | 5.151156e-04 | other | 66 |
| cyperophylla | 9.827502e-05 | 7.358728e-05 | -2.468775e-05 | other | 92 |
| dangarensis | 4.520651e-03 | 7.451232e-03 | 2.930580e-03 | paleo-endemic | 2 |
| dawsonii | 3.013767e-04 | 8.916427e-04 | 5.902659e-04 | other | 30 |
| dealbata | 5.758791e-05 | 1.093708e-05 | -4.665083e-05 | other | 157 |
| deanei | 4.834921e-05 | 8.730399e-06 | -3.961881e-05 | other | 187 |
| debilis | 6.027535e-04 | 5.539579e-04 | -4.879562e-05 | other | 15 |
| declinata | 2.260326e-03 | 1.914810e-03 | -3.455160e-04 | meso-endemic | 4 |
| decora | 3.645686e-05 | 5.971545e-05 | 2.325858e-05 | other | 248 |
| decurrens | 4.520651e-04 | 3.575787e-08 | -4.520294e-04 | other | 20 |
| delibrata | 4.520651e-04 | 3.778769e-04 | -7.418819e-05 | other | 20 |
| delphina | 7.534419e-04 | 4.314350e-03 | 3.560908e-03 | other | 12 |
| dempsteri | 5.022946e-04 | 1.809224e-03 | 1.306929e-03 | other | 18 |
| denticulosa | 1.130163e-03 | 2.010220e-04 | -9.291408e-04 | neo-endemic | 8 |
| desmondii | 1.291615e-03 | 3.279173e-04 | -9.636973e-04 | neo-endemic | 7 |
| diallaga | 4.520651e-03 | 3.270479e-04 | -4.193603e-03 | neo-endemic | 2 |
| dictyoneura | 4.520651e-03 | 3.307158e-03 | -1.213493e-03 | neo-endemic | 2 |
| dictyophleba | 4.783758e-05 | 6.060738e-05 | 1.276980e-05 | other | 189 |
| didyma | 4.520651e-03 | 3.575787e-07 | -4.520294e-03 | neo-endemic | 2 |
| difficilis | 7.597733e-05 | 2.232075e-05 | -5.365658e-05 | other | 119 |
| dimidiata | 1.159141e-04 | 2.612836e-04 | 1.453694e-04 | other | 78 |
| diphylla | 2.260326e-03 | 2.374963e-03 | 1.146377e-04 | meso-endemic | 4 |
| disparrima | 1.063683e-04 | 7.301173e-05 | -3.335653e-05 | other | 85 |
| distans | 6.027535e-04 | 5.797399e-04 | -2.301357e-05 | other | 15 |
| dodonaeifolia | 4.520651e-04 | 6.012640e-04 | 1.491989e-04 | other | 20 |
| dolichophylla | 2.260326e-03 | 1.087952e-02 | 8.619193e-03 | paleo-endemic | 4 |
| doratoxylon | 8.219366e-05 | 1.726829e-04 | 9.048922e-05 | other | 110 |
| dorothea | 9.041302e-04 | 9.912609e-04 | 8.713065e-05 | other | 10 |
| drepanocarpa | 9.618407e-05 | 2.137020e-05 | -7.481386e-05 | other | 94 |
| drepanophylla | 1.130163e-03 | 3.155062e-03 | 2.024899e-03 | meso-endemic | 8 |
| drummondii | 2.443595e-04 | 2.884462e-04 | 4.408670e-05 | other | 37 |
| dunnii | 2.659207e-04 | 3.924237e-04 | 1.265030e-04 | other | 34 |
| elachantha | 5.870976e-05 | 4.643879e-09 | -5.870511e-05 | other | 154 |
| elata | 6.027535e-04 | 1.707331e-03 | 1.104577e-03 | other | 15 |
| elongata | 2.443595e-04 | 1.557358e-04 | -8.862377e-05 | other | 37 |
| empelioclada | 1.808260e-03 | 2.101121e-03 | 2.928602e-04 | meso-endemic | 5 |
| enervia | 1.965500e-04 | 1.332475e-04 | -6.330260e-05 | other | 46 |
| enterocarpa | 5.650814e-04 | 3.796834e-04 | -1.853980e-04 | other | 16 |
| epacantha | 3.013767e-03 | 5.409387e-03 | 2.395620e-03 | meso-endemic | 3 |
| eriopoda | 1.586193e-04 | 7.484815e-04 | 5.898622e-04 | other | 57 |
| errabunda | 1.506884e-03 | 1.690419e-03 | 1.835348e-04 | meso-endemic | 6 |
| estrophiolata | 8.529530e-05 | 1.650084e-04 | 7.971307e-05 | other | 106 |
| euthycarpa | 1.144469e-04 | 9.517772e-05 | -1.926914e-05 | other | 79 |
| excelsa | 4.783758e-05 | 1.838210e-05 | -2.945548e-05 | other | 189 |
| excentrica | 9.041302e-04 | 7.147236e-04 | -1.894066e-04 | other | 10 |
| exilis | 1.506884e-03 | 2.421227e-03 | 9.143433e-04 | meso-endemic | 6 |
| extensa | 3.348630e-04 | 1.416159e-03 | 1.081296e-03 | other | 27 |
| fagonioides | 1.808260e-03 | 3.241301e-03 | 1.433041e-03 | meso-endemic | 5 |
| falcata | 1.015877e-04 | 8.035475e-09 | -1.015796e-04 | other | 89 |
| falciformis | 7.662121e-05 | 4.818377e-05 | -2.843744e-05 | other | 118 |
| faucium | 1.808260e-03 | 7.259642e-04 | -1.082296e-03 | neo-endemic | 5 |
| fecunda | 2.260326e-03 | 2.142886e-03 | -1.174394e-04 | meso-endemic | 4 |
| filicifolia | 2.205196e-04 | 8.371121e-05 | -1.368084e-04 | other | 41 |
| fimbriata | 1.027421e-04 | 1.739064e-05 | -8.535143e-05 | other | 88 |
| flexifolia | 1.923681e-04 | 1.761718e-04 | -1.619629e-05 | other | 47 |
| floribunda | 1.076346e-04 | 1.947956e-04 | 8.716102e-05 | other | 84 |
| fragilis | 2.102628e-04 | 1.452862e-04 | -6.497662e-05 | other | 43 |
| fulva | 1.808260e-03 | 1.324531e-03 | -4.837296e-04 | meso-endemic | 5 |
| gardneri | 9.041302e-04 | 3.984442e-04 | -5.056860e-04 | other | 10 |
| gelasina | 2.260326e-03 | 2.557917e-03 | 2.975918e-04 | meso-endemic | 4 |
| genistifolia | 7.794226e-05 | 1.178698e-04 | 3.992756e-05 | other | 116 |
| georginae | 8.951784e-05 | 1.434821e-04 | 5.396423e-05 | other | 101 |
| gilbertii | 6.954848e-04 | 5.578852e-04 | -1.375996e-04 | other | 13 |
| gillii | 2.260326e-03 | 2.516661e-03 | 2.563354e-04 | meso-endemic | 4 |
| gittinsii | 9.041302e-04 | 1.741342e-03 | 8.372116e-04 | other | 10 |
| gladiiformis | 2.825407e-04 | 5.708986e-04 | 2.883579e-04 | other | 32 |
| glaucissima | 1.808260e-03 | 2.314075e-03 | 5.058149e-04 | meso-endemic | 5 |
| glaucocarpa | 2.054841e-04 | 5.059367e-05 | -1.548905e-04 | other | 44 |
| glaucoptera | 3.013767e-04 | 1.167353e-04 | -1.846415e-04 | other | 30 |
| gnidium | 5.318413e-04 | 3.441696e-04 | -1.876717e-04 | other | 17 |
| gonocarpa | 1.532424e-04 | 8.435170e-04 | 6.902746e-04 | other | 59 |
| gonoclada | 6.747241e-05 | 7.179551e-05 | 4.323101e-06 | other | 134 |
| gonophylla | 3.931001e-04 | 1.091335e-03 | 6.982349e-04 | other | 23 |
| gracillima | 6.954848e-04 | 1.270560e-03 | 5.750757e-04 | other | 13 |
| grandifolia | 1.808260e-03 | 7.015666e-04 | -1.106694e-03 | neo-endemic | 5 |
| grasbyi | 1.458275e-04 | 4.361439e-05 | -1.022131e-04 | other | 62 |
| gregorii | 6.954848e-04 | 3.589778e-03 | 2.894294e-03 | other | 13 |
| guinetii | 4.520651e-03 | 9.092776e-04 | -3.611374e-03 | neo-endemic | 2 |
| hakeoides | 4.367779e-05 | 4.211422e-05 | -1.563566e-06 | other | 207 |
| halliana | 1.458275e-04 | 2.542503e-04 | 1.084229e-04 | other | 62 |
| hamersleyensis | 4.520651e-04 | 6.616731e-04 | 2.096080e-04 | other | 20 |
| hammondii | 6.504534e-05 | 2.785352e-05 | -3.719182e-05 | other | 139 |
| harpophylla | 5.581051e-05 | 1.330010e-05 | -4.251040e-05 | other | 162 |
| harveyi | 6.027535e-04 | 2.449039e-04 | -3.578496e-04 | other | 15 |
| hastulata | 6.458073e-04 | 5.108267e-08 | -6.457562e-04 | other | 14 |
| havilandiorum | 1.390970e-04 | 1.968137e-04 | 5.771677e-05 | other | 65 |
| hemiteles | 1.174195e-04 | 4.068415e-05 | -7.673536e-05 | other | 77 |
| hemsleyi | 1.027421e-04 | 5.079073e-04 | 4.051652e-04 | other | 88 |
| heterochroa | 1.130163e-03 | 2.699382e-03 | 1.569219e-03 | meso-endemic | 8 |
| heteroclita | 3.931001e-04 | 1.466908e-04 | -2.464093e-04 | other | 23 |
| hexaneura | 1.506884e-03 | 1.080625e-03 | -4.262588e-04 | meso-endemic | 6 |
| hilliana | 5.226186e-05 | 2.153507e-05 | -3.072679e-05 | other | 173 |
| holosericea | 2.613093e-05 | 2.066929e-09 | -2.612886e-05 | other | 346 |
| hopperiana | 1.004589e-03 | 7.946192e-08 | -1.004510e-03 | neo-endemic | 9 |
| howittii | 6.458073e-04 | 1.393318e-04 | -5.064755e-04 | other | 14 |
| huegelii | 4.109683e-04 | 1.370513e-03 | 9.595443e-04 | other | 22 |
| hylonoma | 4.520651e-03 | 5.013222e-02 | 4.561157e-02 | paleo-endemic | 2 |
| hypermeces | 9.041302e-03 | 1.074410e-02 | 1.702800e-03 | meso-endemic | 1 |
| hystrix | 7.534419e-04 | 2.479453e-03 | 1.726011e-03 | other | 12 |
| imbricata | 1.808260e-03 | 1.385359e-03 | -4.229019e-04 | meso-endemic | 5 |
| implexa | 4.636565e-05 | 1.265693e-04 | 8.020368e-05 | other | 195 |
| inaequilatera | 1.051314e-04 | 1.812082e-04 | 7.607677e-05 | other | 86 |
| inceana | 3.229037e-04 | 4.895622e-04 | 1.666585e-04 | other | 28 |
| incrassata | 1.004589e-03 | 5.700825e-03 | 4.696236e-03 | paleo-endemic | 9 |
| ingramii | 1.808260e-03 | 1.707909e-03 | -1.003520e-04 | meso-endemic | 5 |
| irrorata | 1.130163e-04 | 1.884847e-05 | -9.416781e-05 | other | 80 |
| iteaphylla | 2.659207e-04 | 5.605052e-04 | 2.945845e-04 | other | 34 |
| ixiophylla | 1.435127e-04 | 9.767902e-05 | -4.583372e-05 | other | 63 |
| ixodes | 2.583229e-04 | 6.562958e-04 | 3.979728e-04 | other | 35 |
| jacksonioides | 7.534419e-04 | 4.555974e-03 | 3.802532e-03 | other | 12 |
| jamesiana | 3.767209e-04 | 6.491544e-04 | 2.724335e-04 | other | 24 |
| jennerae | 1.089314e-04 | 1.572901e-04 | 4.835874e-05 | other | 83 |
| jensenii | 4.758580e-04 | 1.711435e-03 | 1.235577e-03 | other | 19 |
| jibberdingensis | 3.616521e-04 | 8.985223e-05 | -2.717999e-04 | other | 25 |
| jonesii | 1.506884e-03 | 2.427876e-04 | -1.264096e-03 | neo-endemic | 6 |
| jucunda | 2.739789e-04 | 8.145215e-05 | -1.925267e-04 | other | 33 |
| julifera | 6.412271e-05 | 8.789517e-05 | 2.377246e-05 | other | 141 |
| karina | 3.013767e-03 | 7.506891e-04 | -2.263078e-03 | neo-endemic | 3 |
| kempeana | 3.531759e-05 | 9.497744e-06 | -2.581984e-05 | other | 256 |
| kybeanensis | 7.534419e-04 | 5.959644e-08 | -7.533823e-04 | other | 12 |
| lamprocarpa | 8.693560e-05 | 2.295724e-05 | -6.397836e-05 | other | 104 |
| lasiocalyx | 1.159141e-04 | 2.462542e-04 | 1.303401e-04 | other | 78 |
| latescens | 1.738712e-04 | 3.170681e-04 | 1.431969e-04 | other | 52 |
| latipes | 1.643873e-04 | 1.300286e-08 | -1.643743e-04 | other | 55 |
| latisepala | 1.130163e-03 | 3.808536e-03 | 2.678374e-03 | meso-endemic | 8 |
| latzii | 1.291615e-03 | 3.228034e-04 | -9.688112e-04 | neo-endemic | 7 |
| leiocalyx | 5.413953e-05 | 2.768352e-05 | -2.645602e-05 | other | 167 |
| leioderma | 9.041302e-04 | 1.165093e-03 | 2.609632e-04 | other | 10 |
| leptocarpa | 5.686354e-05 | 9.001330e-05 | 3.314976e-05 | other | 159 |
| leptoneura | 2.260326e-03 | 1.281589e-02 | 1.055557e-02 | paleo-endemic | 4 |
| leucoclada | 1.643873e-04 | 1.453395e-04 | -1.904778e-05 | other | 55 |
| leucolobia | 4.109683e-04 | 4.408154e-04 | 2.984710e-05 | other | 22 |
| ligulata | 1.323763e-05 | 9.300050e-06 | -3.937582e-06 | other | 683 |
| linearifolia | 4.109683e-04 | 2.357883e-04 | -1.751800e-04 | other | 22 |
| lineata | 9.132629e-05 | 1.283840e-05 | -7.848788e-05 | other | 99 |
| lineolata | 2.152691e-04 | 3.269648e-04 | 1.116957e-04 | other | 42 |
| linifolia | 6.458073e-04 | 3.512223e-04 | -2.945850e-04 | other | 14 |
| loderi | 2.152691e-04 | 1.507607e-04 | -6.450838e-05 | other | 42 |
| longifolia | 5.581051e-05 | 2.908240e-05 | -2.672810e-05 | other | 162 |
| longispicata | 1.089314e-04 | 5.300418e-04 | 4.211105e-04 | other | 83 |
| longispinea | 1.674315e-04 | 6.547099e-04 | 4.872784e-04 | other | 54 |
| longissima | 2.659207e-04 | 2.123246e-04 | -5.359605e-05 | other | 34 |
| loroloba | 7.534419e-04 | 5.959644e-08 | -7.533823e-04 | other | 12 |
| lycopodiifolia | 1.051314e-04 | 1.541408e-04 | 4.900937e-05 | other | 86 |
| lysiphloia | 4.453843e-05 | 1.610651e-05 | -2.843192e-05 | other | 203 |
| mabellae | 6.954848e-04 | 3.001854e-04 | -3.952994e-04 | other | 13 |
| macdonnellensis | 1.643873e-04 | 4.268892e-04 | 2.625019e-04 | other | 55 |
| mackeyana | 3.348630e-04 | 1.928347e-04 | -1.420283e-04 | other | 27 |
| macnuttiana | 3.013767e-03 | 1.542803e-03 | -1.470965e-03 | neo-endemic | 3 |
| maconochieana | 7.534419e-04 | 1.152927e-03 | 3.994852e-04 | other | 12 |
| macradenia | 2.009178e-04 | 3.950635e-04 | 1.941457e-04 | other | 45 |
| maitlandii | 4.612909e-05 | 1.546431e-04 | 1.085140e-04 | other | 196 |
| mangium | 4.305382e-04 | 3.405511e-08 | -4.305041e-04 | other | 21 |
| maranoensis | 1.808260e-03 | 2.106996e-04 | -1.597561e-03 | neo-endemic | 5 |
| marramamba | 3.477424e-04 | 7.539219e-04 | 4.061795e-04 | other | 26 |
| masliniana | 4.109683e-04 | 5.798577e-04 | 1.688894e-04 | other | 22 |
| mearnsii | 8.610764e-05 | 8.187043e-06 | -7.792060e-05 | other | 105 |
| meisneri | 8.219366e-04 | 7.637584e-05 | -7.455607e-04 | other | 11 |
| melanoxylon | 3.675326e-05 | 3.684181e-05 | 8.855239e-08 | other | 246 |
| melleodora | 3.477424e-05 | 4.303471e-05 | 8.260466e-06 | other | 260 |
| melvillei | 7.727609e-05 | 6.112456e-09 | -7.726998e-05 | other | 117 |
| menzelii | 1.291615e-03 | 2.194496e-03 | 9.028818e-04 | meso-endemic | 7 |
| microbotrya | 1.205507e-04 | 9.535431e-09 | -1.205412e-04 | other | 75 |
| microsperma | 3.477424e-04 | 2.750605e-08 | -3.477149e-04 | other | 26 |
| midgleyi | 5.650814e-04 | 3.779706e-04 | -1.871108e-04 | other | 16 |
| minyura | 9.320930e-05 | 7.372756e-09 | -9.320193e-05 | other | 97 |
| mitchellii | 3.117690e-04 | 5.187479e-04 | 2.069788e-04 | other | 29 |
| mollifolia | 5.022946e-04 | 2.514377e-04 | -2.508569e-04 | other | 18 |
| montana | 9.132629e-05 | 9.294433e-05 | 1.618047e-06 | other | 99 |
| monticola | 4.147386e-05 | 1.021761e-04 | 6.070219e-05 | other | 218 |
| mountfordiae | 9.041302e-04 | 1.821124e-03 | 9.169935e-04 | other | 10 |
| mucronata | 1.015877e-04 | 5.603221e-05 | -4.555546e-05 | other | 89 |
| muelleriana | 3.616521e-04 | 2.709322e-04 | -9.071989e-05 | other | 25 |
| multisiliqua | 6.954848e-05 | 2.406957e-05 | -4.547891e-05 | other | 130 |
| multispicata | 1.174195e-04 | 4.138899e-05 | -7.603052e-05 | other | 77 |
| murrayana | 2.546846e-05 | 1.724498e-05 | -8.223474e-06 | other | 355 |
| myrtifolia | 4.224908e-05 | 1.265250e-04 | 8.427592e-05 | other | 214 |
| nanodealbata | 6.458073e-04 | 2.465476e-04 | -3.992597e-04 | other | 14 |
| nematophylla | 5.022946e-04 | 5.499166e-04 | 4.762204e-05 | other | 18 |
| neriifolia | 8.951784e-05 | 6.424201e-05 | -2.527583e-05 | other | 101 |
| neurocarpa | 1.189645e-04 | 9.409965e-09 | -1.189551e-04 | other | 76 |
| neurophylla | 1.329603e-04 | 2.234059e-04 | 9.044556e-05 | other | 68 |
| nigricans | 1.291615e-03 | 5.549989e-04 | -7.366157e-04 | neo-endemic | 7 |
| notabilis | 1.390970e-04 | 6.622633e-05 | -7.287063e-05 | other | 65 |
| nuperrima | 1.310334e-04 | 3.760527e-04 | 2.450193e-04 | other | 69 |
| nyssophylla | 5.833098e-05 | 3.367499e-05 | -2.465600e-05 | other | 155 |
| obliquinervia | 1.586193e-04 | 1.016175e-04 | -5.700188e-05 | other | 57 |
| obtecta | 8.219366e-04 | 4.835386e-03 | 4.013450e-03 | other | 11 |
| obtusata | 5.022946e-04 | 7.312644e-04 | 2.289698e-04 | other | 18 |
| obtusifolia | 2.205196e-04 | 3.417648e-04 | 1.212452e-04 | other | 41 |
| oldfieldii | 9.041302e-04 | 2.881355e-04 | -6.159947e-04 | other | 10 |
| olgana | 4.305382e-04 | 3.790987e-04 | -5.143951e-05 | other | 21 |
| olsenii | 4.520651e-03 | 3.575787e-07 | -4.520294e-03 | neo-endemic | 2 |
| omalophylla | 8.001152e-05 | 6.328826e-09 | -8.000520e-05 | other | 113 |
| oncinocarpa | 1.329603e-04 | 3.338679e-04 | 2.009076e-04 | other | 68 |
| oncinophylla | 8.219366e-04 | 7.309440e-05 | -7.488422e-04 | other | 11 |
| orites | 2.260326e-03 | 2.625937e-03 | 3.656117e-04 | meso-endemic | 4 |
| orthocarpa | 8.951784e-05 | 1.358819e-04 | 4.636407e-05 | other | 101 |
| oshanesii | 3.616521e-04 | 1.789506e-04 | -1.827015e-04 | other | 25 |
| oswaldii | 1.458275e-05 | 9.687391e-06 | -4.895354e-06 | other | 620 |
| oxyclada | 1.506884e-03 | 8.782038e-04 | -6.286799e-04 | neo-endemic | 6 |
| pachyacra | 1.390970e-04 | 5.051913e-05 | -8.857783e-05 | other | 65 |
| pachycarpa | 4.758580e-04 | 4.834945e-04 | 7.636480e-06 | other | 19 |
| papyrocarpa | 8.072591e-05 | 1.342818e-04 | 5.355590e-05 | other | 112 |
| paradoxa | 4.783758e-05 | 7.922931e-05 | 3.139173e-05 | other | 189 |
| paraneura | 7.350652e-05 | 5.814287e-09 | -7.350071e-05 | other | 123 |
| parramattensis | 2.916549e-04 | 4.829998e-05 | -2.433549e-04 | other | 31 |
| parvipinnula | 5.650814e-04 | 4.284248e-04 | -1.366566e-04 | other | 16 |
| patagiata | 4.305382e-04 | 5.534962e-04 | 1.229580e-04 | other | 21 |
| pedina | 3.013767e-03 | 2.383858e-07 | -3.013529e-03 | neo-endemic | 3 |
| pedleyi | 2.260326e-03 | 7.846074e-04 | -1.475718e-03 | neo-endemic | 4 |
| pellita | 2.009178e-04 | 7.261772e-05 | -1.283001e-04 | other | 45 |
| pendula | 6.278682e-05 | 6.050484e-05 | -2.281981e-06 | other | 144 |
| penninervis | 5.758791e-05 | 8.286367e-06 | -4.930155e-05 | other | 157 |
| pentadenia | 7.534419e-04 | 1.364025e-03 | 6.105834e-04 | other | 12 |
| perangusta | 1.808260e-03 | 2.316820e-04 | -1.576578e-03 | neo-endemic | 5 |
| perryi | 3.348630e-04 | 2.962376e-04 | -3.862548e-05 | other | 27 |
| petraea | 2.511473e-04 | 1.252748e-04 | -1.258725e-04 | other | 36 |
| peuce | 5.022946e-04 | 2.197851e-03 | 1.695556e-03 | other | 18 |
| phlebopetala | 1.130163e-03 | 2.418246e-03 | 1.288083e-03 | meso-endemic | 8 |
| phlebophylla | 3.013767e-03 | 4.054453e-03 | 1.040686e-03 | meso-endemic | 3 |
| pickardii | 1.291615e-03 | 2.351349e-03 | 1.059734e-03 | meso-endemic | 7 |
| platycarpa | 4.834921e-05 | 2.732997e-05 | -2.101924e-05 | other | 187 |
| plectocarpa | 6.235381e-05 | 8.240310e-06 | -5.411350e-05 | other | 145 |
| podalyriifolia | 1.923681e-04 | 9.966441e-05 | -9.270372e-05 | other | 47 |
| polybotrya | 2.511473e-04 | 5.839846e-05 | -1.927488e-04 | other | 36 |
| porcata | 4.520651e-03 | 1.314903e-02 | 8.628374e-03 | paleo-endemic | 2 |
| praelongata | 6.458073e-04 | 1.262040e-03 | 6.162324e-04 | other | 14 |
| prainii | 1.015877e-04 | 3.567385e-05 | -6.591382e-05 | other | 89 |
| pravifolia | 1.329603e-04 | 1.334828e-04 | 5.224732e-07 | other | 68 |
| pravissima | 2.443595e-04 | 2.324177e-04 | -1.194185e-05 | other | 37 |
| producta | 3.229037e-04 | 5.648936e-04 | 2.419900e-04 | other | 28 |
| proiantha | 1.506884e-03 | 1.090421e-03 | -4.164631e-04 | meso-endemic | 6 |
| prominens | 9.041302e-04 | 7.151573e-08 | -9.040587e-04 | other | 10 |
| pruinocarpa | 7.794226e-05 | 3.362288e-04 | 2.582865e-04 | other | 116 |
| pruinosa | 3.931001e-04 | 3.719814e-05 | -3.559020e-04 | other | 23 |
| pterocaulon | 4.520651e-03 | 9.952641e-03 | 5.431989e-03 | paleo-endemic | 2 |
| ptychophylla | 3.616521e-04 | 2.377486e-04 | -1.239035e-04 | other | 25 |
| pubicosta | 1.808260e-03 | 7.195957e-03 | 5.387696e-03 | paleo-endemic | 5 |
| pubifolia | 1.506884e-03 | 1.191929e-07 | -1.506765e-03 | neo-endemic | 6 |
| pulchella | 1.089314e-04 | 7.289968e-04 | 6.200654e-04 | other | 83 |
| pustula | 2.825407e-04 | 4.289275e-05 | -2.396479e-04 | other | 32 |
| pycnantha | 5.795707e-05 | 1.078601e-05 | -4.717106e-05 | other | 156 |
| pycnostachya | 1.506884e-03 | 2.223805e-03 | 7.169213e-04 | meso-endemic | 6 |
| pygmaea | 4.520651e-03 | 2.380212e-02 | 1.928147e-02 | paleo-endemic | 2 |
| pyrifolia | 8.001152e-05 | 6.323233e-05 | -1.677920e-05 | other | 113 |
| ramulosa | 1.991476e-05 | 1.290480e-05 | -7.009963e-06 | other | 454 |
| redolens | 6.458073e-04 | 1.287947e-03 | 6.421395e-04 | other | 14 |
| repanda | 4.520651e-03 | 7.566212e-04 | -3.764030e-03 | neo-endemic | 2 |
| retinervis | 6.027535e-04 | 4.767715e-08 | -6.027058e-04 | other | 15 |
| retinodes | 2.054841e-04 | 2.181258e-04 | 1.264167e-05 | other | 44 |
| retivenea | 6.849471e-05 | 4.696718e-05 | -2.152754e-05 | other | 132 |
| rhamphophylla | 3.013767e-03 | 4.176572e-03 | 1.162805e-03 | meso-endemic | 3 |
| rhetinocarpa | 6.954848e-04 | 2.244641e-04 | -4.710207e-04 | other | 13 |
| rhigiophylla | 5.650814e-04 | 1.189834e-04 | -4.460980e-04 | other | 16 |
| rhodophloia | 8.449815e-05 | 3.112146e-05 | -5.337669e-05 | other | 107 |
| rigens | 4.305382e-05 | 5.642081e-05 | 1.336699e-05 | other | 210 |
| rivalis | 5.022946e-04 | 3.403488e-04 | -1.619458e-04 | other | 18 |
| rostellifera | 1.808260e-04 | 4.492540e-05 | -1.359006e-04 | other | 50 |
| rubida | 9.827502e-05 | 1.802669e-04 | 8.199186e-05 | other | 92 |
| ryaniana | 1.808260e-03 | 1.853731e-03 | 4.547095e-05 | meso-endemic | 5 |
| sabulosa | 4.305382e-04 | 3.405511e-08 | -4.305041e-04 | other | 21 |
| saliciformis | 1.004589e-03 | 1.850418e-04 | -8.195474e-04 | neo-endemic | 9 |
| salicina | 2.366833e-05 | 2.181107e-05 | -1.857257e-06 | other | 382 |
| saligna | 7.662121e-05 | 1.360768e-04 | 5.945559e-05 | other | 118 |
| saxatilis | 1.130163e-03 | 2.626446e-03 | 1.496284e-03 | meso-endemic | 8 |
| schinoides | 1.130163e-03 | 9.455330e-05 | -1.035609e-03 | neo-endemic | 8 |
| scirpifolia | 4.758580e-04 | 3.763986e-08 | -4.758204e-04 | other | 19 |
| sclerophylla | 8.219366e-05 | 1.332951e-04 | 5.110144e-05 | other | 110 |
| semicircinalis | 2.260326e-03 | 8.585448e-03 | 6.325123e-03 | paleo-endemic | 4 |
| semilunata | 5.022946e-04 | 1.195621e-03 | 6.933261e-04 | other | 18 |
| semitrullata | 1.130163e-03 | 3.372326e-03 | 2.242163e-03 | meso-endemic | 8 |
| sericoflora | 5.650814e-04 | 6.895197e-04 | 1.244383e-04 | other | 16 |
| sericophylla | 4.636565e-05 | 3.891030e-05 | -7.455349e-06 | other | 195 |
| sessilispica | 3.348630e-04 | 3.605585e-04 | 2.569547e-05 | other | 27 |
| shuttleworthii | 6.954848e-04 | 1.689644e-03 | 9.941589e-04 | other | 13 |
| sibilans | 4.305382e-04 | 7.772306e-05 | -3.528151e-04 | other | 21 |
| sibina | 2.379290e-04 | 2.373277e-04 | -6.012703e-07 | other | 38 |
| siculiformis | 1.586193e-04 | 3.059698e-03 | 2.901079e-03 | other | 57 |
| silvestris | 5.650814e-04 | 1.294422e-04 | -4.356392e-04 | other | 16 |
| simsii | 7.862002e-05 | 4.165739e-05 | -3.696263e-05 | other | 115 |
| simulans | 2.260326e-03 | 7.527814e-03 | 5.267489e-03 | paleo-endemic | 4 |
| sparsiflora | 1.390970e-04 | 3.417164e-04 | 2.026194e-04 | other | 65 |
| spathulifolia | 3.767209e-04 | 1.379207e-03 | 1.002486e-03 | other | 24 |
| spectabilis | 1.076346e-04 | 3.073131e-04 | 1.996785e-04 | other | 84 |
| spinescens | 1.089314e-04 | 7.008022e-04 | 5.918708e-04 | other | 83 |
| spirorbis | 5.318413e-04 | 2.272885e-04 | -3.045528e-04 | other | 17 |
| spongolitica | 1.291615e-03 | 4.427067e-04 | -8.489079e-04 | neo-endemic | 7 |
| stanleyi | 3.013767e-03 | 2.383858e-07 | -3.013529e-03 | neo-endemic | 3 |
| stenophylla | 3.206136e-05 | 2.239887e-04 | 1.919274e-04 | other | 282 |
| stigmatophylla | 2.205196e-04 | 5.408354e-04 | 3.203159e-04 | other | 41 |
| stipuligera | 6.599491e-05 | 8.888522e-05 | 2.289031e-05 | other | 137 |
| storyi | 1.808260e-03 | 1.532003e-03 | -2.762579e-04 | meso-endemic | 5 |
| stowardii | 6.797972e-05 | 8.512254e-06 | -5.946746e-05 | other | 133 |
| striatifolia | 6.954848e-04 | 2.124921e-04 | -4.829927e-04 | other | 13 |
| strongylophylla | 2.102628e-04 | 3.991736e-04 | 1.889108e-04 | other | 43 |
| suaveolens | 7.410904e-05 | 1.461522e-04 | 7.204318e-05 | other | 122 |
| subrigida | 1.291615e-03 | 3.111243e-03 | 1.819628e-03 | meso-endemic | 7 |
| subsessilis | 1.004589e-03 | 5.491490e-04 | -4.554402e-04 | meso-endemic | 9 |
| subtessarogona | 6.027535e-04 | 2.441324e-04 | -3.586211e-04 | other | 15 |
| subulata | 3.616521e-04 | 2.444876e-04 | -1.171645e-04 | other | 25 |
| sulcaticaulis | 9.041302e-03 | 6.926541e-03 | -2.114761e-03 | neo-endemic | 1 |
| synchronicia | 1.004589e-04 | 2.667700e-04 | 1.663111e-04 | other | 90 |
| tarculensis | 3.931001e-04 | 3.109380e-08 | -3.930690e-04 | other | 23 |
| telmica | 3.013767e-03 | 4.678942e-03 | 1.665175e-03 | meso-endemic | 3 |
| tenuinervis | 1.130163e-03 | 4.150358e-04 | -7.151270e-04 | neo-endemic | 8 |
| tenuispica | 8.219366e-04 | 1.834678e-03 | 1.012742e-03 | other | 11 |
| tenuissima | 4.166499e-05 | 7.344688e-05 | 3.178189e-05 | other | 217 |
| tephrina | 1.310334e-04 | 8.915926e-05 | -4.187410e-05 | other | 69 |
| terminalis | 1.015877e-04 | 4.265061e-04 | 3.249185e-04 | other | 89 |
| tessellata | 1.291615e-03 | 6.442597e-04 | -6.473549e-04 | neo-endemic | 7 |
| tetragonophylla | 1.931902e-05 | 3.665425e-05 | 1.733523e-05 | other | 468 |
| thomsonii | 2.739789e-04 | 2.331303e-05 | -2.506658e-04 | other | 33 |
| tindaleae | 6.458073e-04 | 2.755623e-04 | -3.702450e-04 | other | 14 |
| torulosa | 4.520651e-05 | 3.964312e-05 | -5.563396e-06 | other | 200 |
| trachycarpa | 2.260326e-04 | 1.787893e-08 | -2.260147e-04 | other | 40 |
| trachyphloia | 1.291615e-03 | 5.363242e-04 | -7.552905e-04 | neo-endemic | 7 |
| translucens | 1.238535e-04 | 7.344421e-05 | -5.040925e-05 | other | 73 |
| triptera | 1.329603e-04 | 1.211247e-04 | -1.183564e-05 | other | 68 |
| triquetra | 3.477424e-04 | 1.480797e-04 | -1.996627e-04 | other | 26 |
| tropica | 1.965500e-04 | 4.125094e-04 | 2.159593e-04 | other | 46 |
| tumida | 4.432011e-05 | 6.356455e-05 | 1.924444e-05 | other | 204 |
| tysonii | 3.477424e-04 | 2.221210e-04 | -1.256214e-04 | other | 26 |
| umbellata | 8.072591e-05 | 1.546654e-04 | 7.393949e-05 | other | 112 |
| umbraculiformis | 3.477424e-04 | 2.750605e-08 | -3.477149e-04 | other | 26 |
| uncinata | 9.041302e-04 | 1.062119e-03 | 1.579891e-04 | other | 10 |
| undoolyana | 2.260326e-03 | 3.808520e-04 | -1.879474e-03 | neo-endemic | 4 |
| validinervia | 1.586193e-04 | 5.343143e-04 | 3.756950e-04 | other | 57 |
| venulosa | 3.013767e-04 | 1.216959e-04 | -1.796809e-04 | other | 30 |
| verniciflua | 6.367114e-05 | 1.507505e-05 | -4.859609e-05 | other | 142 |
| verricula | 2.009178e-04 | 2.818688e-04 | 8.095097e-05 | other | 45 |
| vestita | 5.022946e-04 | 3.078380e-04 | -1.944566e-04 | other | 18 |
| victoriae | 1.511923e-05 | 4.273991e-05 | 2.762068e-05 | other | 598 |
| viscidula | 2.102628e-04 | 1.248679e-03 | 1.038416e-03 | other | 43 |
| wanyu | 3.013767e-04 | 6.499338e-05 | -2.363834e-04 | other | 30 |
| wattsiana | 8.219366e-04 | 6.501430e-08 | -8.218716e-04 | other | 11 |
| wilhelmiana | 1.076346e-04 | 7.749416e-05 | -3.014039e-05 | other | 84 |
| woodmaniorum | 3.013767e-03 | 5.004156e-03 | 1.990388e-03 | meso-endemic | 3 |
| xanthina | 1.004589e-03 | 4.607729e-04 | -5.438163e-04 | neo-endemic | 9 |
| xiphophylla | 1.923681e-04 | 2.993527e-04 | 1.069845e-04 | other | 47 |
| yirrkallensis | 3.117690e-04 | 5.226377e-04 | 2.108686e-04 | other | 29 |